WEBVTT 1 00:00:08.000 --> 00:00:10.960 - Hello, and welcome to the Physics World weekly 2 00:00:10.960 --> 00:00:13.059 - podcast. I'm Hamish Johnston. 3 00:00:13.679 --> 00:00:15.779 - My guest is David Wheater, 4 00:00:16.295 --> 00:00:17.675 - who is vice president 5 00:00:18.054 --> 00:00:18.554 - automotive 6 00:00:19.175 --> 00:00:22.155 - at UK and US based PhysicsX. 7 00:00:23.574 --> 00:00:24.394 - An aeronautical 8 00:00:24.855 --> 00:00:27.734 - engineer by training, Dave spent much of his 9 00:00:27.734 --> 00:00:29.515 - career using aerodynamics 10 00:00:29.975 --> 00:00:31.355 - to improve the performance 11 00:00:31.940 --> 00:00:35.719 - of formula one racing cars for several different 12 00:00:35.859 --> 00:00:37.079 - f one constructors. 13 00:00:38.020 --> 00:00:40.520 - Now his focus is on using 14 00:00:40.899 --> 00:00:43.399 - AI based large physics models 15 00:00:43.780 --> 00:00:47.255 - to design better passenger cars and other vehicles. 16 00:00:48.034 --> 00:00:49.414 - This involves combining 17 00:00:49.954 --> 00:00:50.454 - computational 18 00:00:50.835 --> 00:00:51.655 - fluid dynamics 19 00:00:51.954 --> 00:00:52.454 - simulations 20 00:00:53.234 --> 00:00:57.254 - with wind tunnel experiments and real world testing. 21 00:00:57.715 --> 00:01:01.254 - As Dave explains in this wide ranging conversation, 22 00:01:01.634 --> 00:01:04.269 - which also looks at career opportunities 23 00:01:04.729 --> 00:01:05.629 - for physicists 24 00:01:06.250 --> 00:01:07.469 - in this multidisciplinary 25 00:01:08.329 --> 00:01:08.829 - field. 26 00:01:16.115 --> 00:01:18.055 - Hi, Dave. Welcome to the podcast. 27 00:01:19.314 --> 00:01:21.155 - Hi, Hamish. Thank you very much for having 28 00:01:21.155 --> 00:01:21.655 - me. 29 00:01:22.195 --> 00:01:24.435 - So Dave, can we start out? Can you 30 00:01:24.435 --> 00:01:26.295 - tell us a bit about yourself? 31 00:01:27.954 --> 00:01:30.034 - Who who are you? What's your background? And, 32 00:01:31.520 --> 00:01:33.939 - what's your experience in the automotive industry? 33 00:01:35.520 --> 00:01:36.020 - Sure. 34 00:01:36.560 --> 00:01:40.159 - So I am technical director for automotive at 35 00:01:40.159 --> 00:01:40.659 - PhysX. 36 00:01:41.600 --> 00:01:44.240 - That means I I lead automotive activities and 37 00:01:44.240 --> 00:01:45.859 - also competition engineering, 38 00:01:47.055 --> 00:01:48.114 - based on my background. 39 00:01:49.055 --> 00:01:50.674 - So what does that involve? 40 00:01:51.055 --> 00:01:52.114 - Working with customers, 41 00:01:52.655 --> 00:01:53.474 - on the ground. 42 00:01:54.734 --> 00:01:56.674 - You know, we we co engineer 43 00:01:57.055 --> 00:01:59.375 - our our solutions with with customers in order 44 00:01:59.375 --> 00:02:00.515 - to kind of quickly, 45 00:02:01.329 --> 00:02:02.709 - build value. We have 46 00:02:03.090 --> 00:02:06.469 - experienced engineering teams, cross functional teams across 47 00:02:06.770 --> 00:02:10.050 - engineering, machine learning, data science, and software. And, 48 00:02:10.050 --> 00:02:10.870 - you know, living 49 00:02:11.250 --> 00:02:13.349 - side by side with our customers on-site, 50 00:02:13.854 --> 00:02:15.534 - you know, we we we really learn about 51 00:02:15.534 --> 00:02:17.634 - the real world of our customers and, 52 00:02:18.415 --> 00:02:19.615 - and and what we need to do in 53 00:02:19.615 --> 00:02:20.514 - the hands of engineers. 54 00:02:20.895 --> 00:02:22.675 - So I I work there, and I also 55 00:02:23.055 --> 00:02:25.455 - coordinate across the other key pillars of our 56 00:02:25.455 --> 00:02:25.955 - company 57 00:02:26.335 --> 00:02:26.995 - for automotive. 58 00:02:27.689 --> 00:02:29.849 - We have a research group. We'll talk later 59 00:02:29.849 --> 00:02:31.870 - about their work on the large physics model. 60 00:02:32.090 --> 00:02:34.430 - But, yeah, that's a really world class group, 61 00:02:34.810 --> 00:02:37.849 - contributing to academia. For example, a spotlight paper 62 00:02:37.849 --> 00:02:38.509 - at NeurIPS, 63 00:02:38.810 --> 00:02:40.750 - one of the leading machine learning conferences. 64 00:02:41.764 --> 00:02:43.525 - Also, you know, keeping us at the cutting 65 00:02:43.525 --> 00:02:45.444 - edge of research for the benefit of our 66 00:02:45.444 --> 00:02:45.944 - customers. 67 00:02:46.805 --> 00:02:48.025 - Recent example there, 68 00:02:48.724 --> 00:02:51.525 - well, very recent, in fact, Transolver three, which 69 00:02:51.525 --> 00:02:53.865 - is a a kind of machine learning architecture 70 00:02:54.245 --> 00:02:55.319 - similar to a, 71 00:02:56.040 --> 00:02:57.099 - sort of transformer 72 00:02:57.480 --> 00:03:00.540 - used in large languages but architected for physics. 73 00:03:01.159 --> 00:03:01.900 - That was, 74 00:03:02.840 --> 00:03:05.480 - published in academia late February, and we're already 75 00:03:05.480 --> 00:03:07.260 - shipping that onto our platform. 76 00:03:07.835 --> 00:03:10.235 - And then finally, you know, we've got an 77 00:03:10.235 --> 00:03:10.735 - amazing, 78 00:03:11.435 --> 00:03:12.974 - AI and and and product 79 00:03:13.275 --> 00:03:15.915 - group who who build a platform that that 80 00:03:15.915 --> 00:03:17.995 - everything is based on and that allows us 81 00:03:17.995 --> 00:03:20.895 - to deploy our end to end AI workflows 82 00:03:21.034 --> 00:03:22.014 - into our customers' 83 00:03:22.590 --> 00:03:24.689 - most most difficult physics problems. 84 00:03:25.790 --> 00:03:27.389 - And and and there, you know, we need 85 00:03:27.389 --> 00:03:29.310 - to make sure that we're integrating with the 86 00:03:29.310 --> 00:03:31.389 - way our customers already work. So a lot 87 00:03:31.389 --> 00:03:32.290 - of work around, 88 00:03:34.030 --> 00:03:37.069 - kind of hook hooks into existing simulation and 89 00:03:37.069 --> 00:03:37.730 - CAD tools 90 00:03:38.645 --> 00:03:40.824 - and the right level accessibility for engineers. 91 00:03:41.125 --> 00:03:43.064 - So, you know, my my work, 92 00:03:43.604 --> 00:03:46.504 - the customers and understanding the customer problems is 93 00:03:46.805 --> 00:03:48.884 - is really useful for for working with those 94 00:03:48.884 --> 00:03:49.384 - groups. 95 00:03:50.245 --> 00:03:52.264 - I should say I've I've been at PhysicsX 96 00:03:52.324 --> 00:03:53.305 - for nine months, 97 00:03:54.389 --> 00:03:56.069 - and and and it's been, you know, an 98 00:03:56.069 --> 00:03:56.969 - amazing experience, 99 00:03:57.829 --> 00:04:00.329 - working with that kind of diverse group. 100 00:04:01.109 --> 00:04:03.430 - And and prior to PhysicsX, I I I 101 00:04:03.430 --> 00:04:05.269 - spent quite a few years, twenty five years 102 00:04:05.269 --> 00:04:06.405 - in Formula One, 103 00:04:07.365 --> 00:04:09.064 - starting my life as an aerodynamicist 104 00:04:09.685 --> 00:04:10.584 - at at Benetton. 105 00:04:11.764 --> 00:04:12.264 - And, 106 00:04:12.724 --> 00:04:14.965 - by the mid two thousands, I was leading 107 00:04:14.965 --> 00:04:15.625 - the aerodynamics 108 00:04:15.925 --> 00:04:16.425 - development 109 00:04:16.805 --> 00:04:19.064 - across half the car for Renault f one. 110 00:04:19.650 --> 00:04:20.850 - You know, that was still in the days 111 00:04:20.850 --> 00:04:23.430 - of fairly small aerodynamics departments, but 112 00:04:23.810 --> 00:04:24.790 - we were an innovative 113 00:04:25.170 --> 00:04:26.310 - group and and launched 114 00:04:26.850 --> 00:04:27.730 - 2005 115 00:04:27.730 --> 00:04:29.509 - with with some pretty novel ideas. 116 00:04:30.050 --> 00:04:33.009 - One of which, coincidentally, was developed by our 117 00:04:33.009 --> 00:04:33.509 - cofounder, 118 00:04:33.875 --> 00:04:36.134 - Robin Talui, which was a a mass damper. 119 00:04:37.154 --> 00:04:39.014 - And I worked on a 120 00:04:39.395 --> 00:04:42.035 - a a novel rear suspension system, which we 121 00:04:42.035 --> 00:04:44.514 - introduced that year to replicate the error effect 122 00:04:44.514 --> 00:04:45.955 - of of some of the floor that had 123 00:04:45.955 --> 00:04:48.055 - been removed due to regulation changes. 124 00:04:49.300 --> 00:04:51.400 - And and that really kind of cemented, 125 00:04:51.779 --> 00:04:52.520 - the championship 126 00:04:52.900 --> 00:04:54.180 - win in 2005 127 00:04:54.180 --> 00:04:55.220 - and 2006, 128 00:04:55.220 --> 00:04:56.980 - and that was at a time when you 129 00:04:56.980 --> 00:04:59.560 - could win championships with with novel ideas. 130 00:05:00.020 --> 00:05:01.939 - If I jump forward to my later life 131 00:05:01.939 --> 00:05:03.014 - in Formula one, 132 00:05:03.574 --> 00:05:06.694 - 2018, I became aerodynamics director at Williams and 133 00:05:06.694 --> 00:05:07.194 - then, 134 00:05:07.814 --> 00:05:09.915 - aerodynamics technical director at Alpine. 135 00:05:10.455 --> 00:05:13.035 - Much larger departments by then, real machines 136 00:05:13.415 --> 00:05:14.395 - of of development 137 00:05:14.855 --> 00:05:18.155 - where winning takes just relentless pursuit of performance 138 00:05:18.375 --> 00:05:18.990 - over time. 139 00:05:20.430 --> 00:05:22.589 - And and that's where I I met PhysicsX, 140 00:05:22.589 --> 00:05:24.509 - and and they stepped into the picture with 141 00:05:24.509 --> 00:05:25.569 - me as a customer. 142 00:05:27.310 --> 00:05:29.709 - We did some exciting work together. I can't 143 00:05:29.709 --> 00:05:31.709 - talk about the details, but, yeah, the the 144 00:05:31.709 --> 00:05:32.209 - impact 145 00:05:32.589 --> 00:05:34.050 - was what caught my attention, 146 00:05:34.995 --> 00:05:36.834 - not just that we we could do things 147 00:05:36.834 --> 00:05:37.495 - much faster, 148 00:05:38.274 --> 00:05:40.595 - but but the real transformation came from what 149 00:05:40.595 --> 00:05:42.774 - we could do differently with that speed. 150 00:05:43.555 --> 00:05:45.254 - In our case, reacting to results, 151 00:05:45.954 --> 00:05:47.314 - over a race weekend in a in a 152 00:05:47.314 --> 00:05:49.334 - way that just wasn't possible before. 153 00:05:49.759 --> 00:05:51.519 - And and, David, I I think it's easy 154 00:05:51.519 --> 00:05:54.339 - for a physicist to imagine how aerodynamics 155 00:05:54.800 --> 00:05:58.399 - can be very important in Formula one. But 156 00:05:58.399 --> 00:06:01.199 - what about the design of of of modern 157 00:06:01.199 --> 00:06:02.959 - cars, you know, the sort of car that 158 00:06:02.959 --> 00:06:04.979 - I would drive to the supermarket? 159 00:06:05.865 --> 00:06:09.944 - Can significant improvements in fuel efficiency, handling, and 160 00:06:09.944 --> 00:06:10.444 - safety 161 00:06:10.824 --> 00:06:13.805 - still be gained by improving aerodynamics? 162 00:06:15.704 --> 00:06:18.024 - Yeah. Absolutely. And and and as you say, 163 00:06:18.024 --> 00:06:20.550 - Hamish, you know, in in in Formula one, 164 00:06:20.709 --> 00:06:23.449 - aerodynamics and downforce is is is the currency. 165 00:06:23.589 --> 00:06:24.789 - You know, 11% 166 00:06:24.789 --> 00:06:26.870 - of downforce is a tenth of a second, 167 00:06:26.870 --> 00:06:28.569 - and and that can be a a championship 168 00:06:28.629 --> 00:06:30.550 - changer for for a team in a tight 169 00:06:30.550 --> 00:06:31.050 - championship. 170 00:06:32.550 --> 00:06:34.069 - And and I should say in, you know, 171 00:06:34.069 --> 00:06:34.514 - in, 172 00:06:35.074 --> 00:06:37.095 - Formula One, it's not just about the downforce. 173 00:06:37.795 --> 00:06:40.035 - It's also about the drivability. You know, drivers 174 00:06:40.115 --> 00:06:42.275 - but they don't drive to the peak. They 175 00:06:42.275 --> 00:06:44.295 - drive to the trough if you've got variations 176 00:06:44.435 --> 00:06:46.295 - in downforce over over car conditions. 177 00:06:47.699 --> 00:06:48.439 - And so 178 00:06:49.300 --> 00:06:50.120 - in automotive, 179 00:06:51.539 --> 00:06:52.839 - kind of passenger vehicles, 180 00:06:53.300 --> 00:06:56.099 - aerodynamics plays just as critical a role in 181 00:06:56.099 --> 00:06:56.919 - in in slightly 182 00:06:57.459 --> 00:06:58.360 - different ways, 183 00:06:59.300 --> 00:07:01.495 - and gets into just as many systems on 184 00:07:01.495 --> 00:07:03.334 - the car. The obvious one being, of course, 185 00:07:03.334 --> 00:07:06.454 - the external shape, the focus there more on 186 00:07:06.454 --> 00:07:08.954 - drag than than downforce from Formula one. 187 00:07:09.334 --> 00:07:11.514 - You know, above 30 miles an hour, 188 00:07:12.149 --> 00:07:14.230 - most of most of your energy consumption is 189 00:07:14.230 --> 00:07:16.649 - is going into overcoming air resistance. So 190 00:07:16.949 --> 00:07:17.930 - good fuel efficiency 191 00:07:18.230 --> 00:07:21.430 - absolutely needs great aerodynamics, whether that's kind of 192 00:07:21.430 --> 00:07:23.269 - the obvious details that you see on the 193 00:07:23.269 --> 00:07:24.649 - car, the spoilers, etcetera, 194 00:07:24.995 --> 00:07:26.675 - or or the lot less obvious like the 195 00:07:26.675 --> 00:07:28.754 - tire shapes, which can can be real really 196 00:07:28.754 --> 00:07:29.254 - important. 197 00:07:30.115 --> 00:07:31.814 - But also in the thermal management, 198 00:07:32.274 --> 00:07:33.415 - systems like powertrains, 199 00:07:34.194 --> 00:07:37.314 - NVH, which is the noise vibration harshness, and 200 00:07:37.314 --> 00:07:39.875 - that's about controlling the turbulence of the flow 201 00:07:39.875 --> 00:07:40.680 - around the car, 202 00:07:41.240 --> 00:07:44.039 - and, of course, passenger comfort, HVAC, which is 203 00:07:44.039 --> 00:07:45.579 - heating, ventilation, and aircon. 204 00:07:46.199 --> 00:07:48.759 - Those are really important attributes for for, you 205 00:07:48.759 --> 00:07:50.860 - know, OEMs developing their cars, 206 00:07:51.479 --> 00:07:53.180 - and and, you know, pitching them, 207 00:07:54.120 --> 00:07:55.180 - at the top of the market. 208 00:07:56.865 --> 00:08:00.084 - And so today, how how are vehicle 209 00:08:00.384 --> 00:08:00.884 - aerodynamics 210 00:08:01.985 --> 00:08:02.485 - simulated? 211 00:08:03.024 --> 00:08:05.425 - Is it, is it sort of a I'm 212 00:08:05.425 --> 00:08:07.685 - guessing you're you're using some pretty powerful, 213 00:08:08.865 --> 00:08:09.845 - computer models, 214 00:08:10.699 --> 00:08:13.020 - to simulate how, you know, for example, how 215 00:08:13.020 --> 00:08:15.660 - air flows around a car, how it interacts 216 00:08:15.660 --> 00:08:17.759 - with the tires, that sort of thing? 217 00:08:19.500 --> 00:08:20.000 - Absolutely. 218 00:08:20.300 --> 00:08:20.699 - So, 219 00:08:22.060 --> 00:08:22.720 - you know, 220 00:08:24.785 --> 00:08:25.285 - traditionally, 221 00:08:26.545 --> 00:08:28.085 - over the last few decades, 222 00:08:28.785 --> 00:08:31.285 - we've been using these numerical simulations 223 00:08:31.905 --> 00:08:34.725 - for for fluid dynamics or computational fluid dynamics. 224 00:08:35.504 --> 00:08:37.365 - And and these, you know, these simulations 225 00:08:38.049 --> 00:08:38.549 - take 226 00:08:38.850 --> 00:08:41.170 - the the the flow that governs their dynamics, 227 00:08:41.170 --> 00:08:42.950 - the Navios Stokes equations generally, 228 00:08:43.330 --> 00:08:45.110 - and and applies them to 229 00:08:45.490 --> 00:08:46.149 - a discretized 230 00:08:46.769 --> 00:08:49.809 - car and and environment so that a computer 231 00:08:49.809 --> 00:08:50.950 - can solve that numerically. 232 00:08:51.394 --> 00:08:53.075 - And and as you suggest, you know, that 233 00:08:53.075 --> 00:08:54.855 - that that takes a lot of compute horsepower. 234 00:08:55.955 --> 00:08:58.375 - You know, the the the simulations can run, 235 00:08:59.235 --> 00:09:02.615 - if if if you're talking about, unsteady simulations, 236 00:09:02.835 --> 00:09:04.934 - they they they can run for days. 237 00:09:06.199 --> 00:09:08.039 - And and, you know, to get even to 238 00:09:08.039 --> 00:09:09.879 - get to those simulations, you obviously need to 239 00:09:09.879 --> 00:09:11.559 - draw a car and that's likely to be 240 00:09:11.559 --> 00:09:13.879 - in CAD and take some time. But these 241 00:09:13.879 --> 00:09:16.679 - numerical simulations are are pretty fickle. You know, 242 00:09:16.679 --> 00:09:18.940 - to get those numerics to work, 243 00:09:19.485 --> 00:09:21.725 - you need a perfectly closed volume to to 244 00:09:21.725 --> 00:09:24.285 - to solve the calculations, and that actually often 245 00:09:24.285 --> 00:09:26.365 - takes multiple days of work to go from 246 00:09:26.365 --> 00:09:28.205 - something that looks, you know, like a kind 247 00:09:28.205 --> 00:09:30.605 - of finished car on the screen to something 248 00:09:30.605 --> 00:09:32.365 - that you can get through a simulation. And 249 00:09:32.365 --> 00:09:34.049 - and and there's a hidden effort there, which 250 00:09:34.209 --> 00:09:34.709 - aerodynamic 251 00:09:35.089 --> 00:09:35.589 - aerodynamicists 252 00:09:35.889 --> 00:09:37.909 - like myself know only too well. 253 00:09:38.769 --> 00:09:41.110 - So so when we start talking about these 254 00:09:41.250 --> 00:09:43.169 - AI tools, you know, an important part of 255 00:09:43.169 --> 00:09:45.649 - speed up is is also in the the 256 00:09:45.649 --> 00:09:46.789 - the kind of the preparation 257 00:09:48.095 --> 00:09:49.794 - effort that goes into to 258 00:09:50.254 --> 00:09:51.315 - a a current simulation. 259 00:09:51.774 --> 00:09:53.774 - There there's also the post processing side. So, 260 00:09:53.774 --> 00:09:55.554 - you know, once you run that that simulation, 261 00:09:56.735 --> 00:09:58.735 - you'll you'll want to extract the the things 262 00:09:58.735 --> 00:10:00.095 - you really care about. It might be the 263 00:10:00.095 --> 00:10:01.394 - lift. It might be the downforce. 264 00:10:02.610 --> 00:10:04.450 - So so so that you generally set up 265 00:10:04.450 --> 00:10:06.309 - some kind of process around that. 266 00:10:07.250 --> 00:10:09.730 - And and also maybe to understand the flow 267 00:10:09.730 --> 00:10:12.230 - field, you you might take a few slices, 268 00:10:13.009 --> 00:10:14.595 - through through the airflow, 269 00:10:15.134 --> 00:10:16.835 - which you'd sort of save down, 270 00:10:17.615 --> 00:10:20.115 - because that that simulation file is normally 271 00:10:20.495 --> 00:10:21.315 - pretty huge, 272 00:10:22.095 --> 00:10:23.634 - and impractical to save. 273 00:10:24.735 --> 00:10:26.434 - And you said these are big calculations. 274 00:10:26.815 --> 00:10:29.100 - Are can can you do them in house? 275 00:10:29.659 --> 00:10:32.220 - Or do you have to rent time on 276 00:10:32.220 --> 00:10:33.039 - a supercomputer? 277 00:10:33.500 --> 00:10:35.679 - Or do you do a bit of both? 278 00:10:37.179 --> 00:10:38.159 - Yeah. I mean, 279 00:10:39.500 --> 00:10:40.000 - most, 280 00:10:40.860 --> 00:10:43.259 - kind of most automotive players will will will 281 00:10:43.259 --> 00:10:45.495 - have their HPC resource. 282 00:10:46.434 --> 00:10:48.674 - Oft often that is in house, and, you 283 00:10:48.674 --> 00:10:51.575 - know, these are these are big big investments, 284 00:10:52.595 --> 00:10:53.654 - but but increasingly, 285 00:10:54.355 --> 00:10:55.575 - kind of cloud provision, 286 00:10:56.274 --> 00:10:59.095 - and and as so it's moving towards GPUs, 287 00:11:00.160 --> 00:11:01.779 - on on GPU clusters, 288 00:11:04.080 --> 00:11:04.899 - are pretty typical. 289 00:11:07.440 --> 00:11:08.420 - And what about, 290 00:11:09.279 --> 00:11:11.220 - you know, so I I suppose proving 291 00:11:11.679 --> 00:11:13.860 - the the the worth of these simulations. 292 00:11:14.544 --> 00:11:16.384 - How do you do do you have to 293 00:11:16.384 --> 00:11:16.884 - compare 294 00:11:17.665 --> 00:11:19.524 - experimental data and, 295 00:11:20.625 --> 00:11:21.125 - simulations 296 00:11:21.504 --> 00:11:24.245 - in order to to know that you're on 297 00:11:24.465 --> 00:11:25.825 - the right track? I mean, do do you 298 00:11:25.825 --> 00:11:27.285 - have to actually build a model 299 00:11:27.710 --> 00:11:29.309 - of the car and and stick it in 300 00:11:29.309 --> 00:11:31.309 - a wind tunnel? I'm guessing that's probably what 301 00:11:31.309 --> 00:11:33.389 - you're trying to avoid because that would be 302 00:11:33.389 --> 00:11:36.610 - very expensive. So how how do you confirm 303 00:11:36.670 --> 00:11:38.610 - that your that your simulations 304 00:11:38.910 --> 00:11:39.730 - are working? 305 00:11:41.470 --> 00:11:41.970 - Yeah. 306 00:11:42.855 --> 00:11:43.355 - Absolutely, 307 00:11:44.534 --> 00:11:45.034 - experiments 308 00:11:45.975 --> 00:11:48.134 - are an important part of the picture. And, 309 00:11:48.534 --> 00:11:51.174 - if I kind of think about my formula 310 00:11:51.174 --> 00:11:51.835 - one days, 311 00:11:53.174 --> 00:11:55.495 - then, you know, we we we were using 312 00:11:55.495 --> 00:11:56.394 - the wind tunnel 313 00:11:57.549 --> 00:12:01.649 - very heavily to kind of correlate and tune 314 00:12:02.110 --> 00:12:03.629 - our our CFD. In the end, you know, 315 00:12:03.629 --> 00:12:05.570 - the CFD is a is a model of 316 00:12:05.710 --> 00:12:07.089 - a a model of your reality. 317 00:12:07.950 --> 00:12:10.370 - And and so, you know, having that experimental 318 00:12:10.509 --> 00:12:11.009 - data, 319 00:12:11.629 --> 00:12:14.004 - albeit it's, you know, it's it's time consuming 320 00:12:14.004 --> 00:12:15.305 - and it's and it's expensive 321 00:12:15.764 --> 00:12:17.945 - is is an important part of validation 322 00:12:18.245 --> 00:12:18.985 - for sure. 323 00:12:20.805 --> 00:12:22.325 - I see. But, 324 00:12:22.725 --> 00:12:24.245 - and and and so now I mean I 325 00:12:24.245 --> 00:12:26.325 - mean, maybe this is a good time to 326 00:12:26.325 --> 00:12:27.785 - to start talking about 327 00:12:28.320 --> 00:12:28.820 - AI. 328 00:12:29.279 --> 00:12:32.000 - So, David, before the advent of AI, how 329 00:12:32.000 --> 00:12:32.899 - were experimental 330 00:12:33.360 --> 00:12:34.180 - and simulated 331 00:12:34.639 --> 00:12:35.139 - aerodynamics 332 00:12:35.680 --> 00:12:38.259 - data used to design cars? 333 00:12:39.920 --> 00:12:41.300 - Great question. Thanks, Hamish. 334 00:12:42.345 --> 00:12:44.985 - So during my f one experience, although I 335 00:12:44.985 --> 00:12:47.465 - think the answer is similar for for sort 336 00:12:47.465 --> 00:12:49.004 - of passenger cars as well. 337 00:12:49.384 --> 00:12:51.384 - You know, when I started at Benetton in 338 00:12:51.384 --> 00:12:53.945 - 2000, it was really all about wind tunnel 339 00:12:53.945 --> 00:12:54.445 - testing, 340 00:12:55.350 --> 00:12:57.910 - and just doing that as much as possible 341 00:12:57.910 --> 00:12:59.210 - and as fast as possible. 342 00:12:59.590 --> 00:13:02.730 - But it was expensive. And, you know, even 343 00:13:03.029 --> 00:13:05.029 - with, you know, f one speed, it was 344 00:13:05.029 --> 00:13:07.450 - relatively slow weeks between loops 345 00:13:07.995 --> 00:13:10.235 - and limited in terms of the feedback that 346 00:13:10.235 --> 00:13:10.975 - you could get, 347 00:13:11.434 --> 00:13:13.835 - from those tests. You you'd get force from 348 00:13:13.835 --> 00:13:16.014 - the balance and maybe a few pressure sensors. 349 00:13:17.514 --> 00:13:20.315 - Then on the CFD side, we had, at 350 00:13:20.315 --> 00:13:22.659 - that stage, just one engineer, and their entire 351 00:13:22.659 --> 00:13:24.899 - focus actually was on the the airbox, which 352 00:13:24.899 --> 00:13:26.659 - is is the sort of feed behind the 353 00:13:26.659 --> 00:13:28.440 - driver's head going to the engine. 354 00:13:28.820 --> 00:13:30.899 - Otherwise, it was all wind tunnel. And there 355 00:13:30.899 --> 00:13:32.659 - was a lot of skepticism at the time 356 00:13:32.659 --> 00:13:34.600 - about that simulation with engineers 357 00:13:35.125 --> 00:13:37.225 - saying they didn't believe the CFD and and 358 00:13:37.605 --> 00:13:38.585 - not using it. 359 00:13:38.965 --> 00:13:40.904 - And then through the two thousands, 360 00:13:41.845 --> 00:13:43.445 - you know, we we saw that the the 361 00:13:43.445 --> 00:13:45.524 - teams that really thrived were the ones that 362 00:13:45.524 --> 00:13:47.465 - saw CFD as something complimentary. 363 00:13:47.909 --> 00:13:50.069 - You know, it was much, much faster than 364 00:13:50.069 --> 00:13:52.629 - than wind tunnel testing. It provided flow field 365 00:13:52.629 --> 00:13:54.470 - insight that that you couldn't get from the 366 00:13:54.470 --> 00:13:55.209 - wind tunnel. 367 00:13:55.669 --> 00:13:57.669 - So with results in a day rather than 368 00:13:57.669 --> 00:14:00.870 - weeks, that massively, you know, accelerated how you 369 00:14:00.870 --> 00:14:03.085 - could develop the car, and that insight that 370 00:14:03.085 --> 00:14:06.384 - you got really helped inform next steps. So 371 00:14:06.925 --> 00:14:08.845 - then if if we roll through to sort 372 00:14:08.845 --> 00:14:09.825 - of 2020, 373 00:14:11.245 --> 00:14:11.745 - then 374 00:14:12.125 --> 00:14:14.225 - your CFD was absolutely a given. 375 00:14:14.580 --> 00:14:15.480 - And and and, actually, 376 00:14:16.820 --> 00:14:19.059 - the the majority of team principals in f 377 00:14:19.059 --> 00:14:21.320 - one voted in an f one meeting 378 00:14:21.700 --> 00:14:24.019 - to turn off wind tunnels completely for for 379 00:14:24.019 --> 00:14:26.500 - cost saving. But, actually, I think that that 380 00:14:26.500 --> 00:14:28.740 - kinda misses the point, to one of your 381 00:14:28.740 --> 00:14:29.720 - previous questions. 382 00:14:30.054 --> 00:14:32.134 - You know, the these these tools are are 383 00:14:32.134 --> 00:14:34.695 - best when you use them together. And I 384 00:14:34.695 --> 00:14:37.095 - think we're living through the same moment with 385 00:14:37.095 --> 00:14:39.274 - with AI. You know, there's absolutely, 386 00:14:40.054 --> 00:14:40.795 - some skepticism, 387 00:14:42.615 --> 00:14:45.070 - and and and some some engineers see this 388 00:14:45.070 --> 00:14:46.990 - as a swap in for CFD. But I 389 00:14:46.990 --> 00:14:49.870 - think the most successful users I see are 390 00:14:49.870 --> 00:14:52.429 - are those that are are using AI as 391 00:14:52.429 --> 00:14:53.250 - another layer, 392 00:14:53.950 --> 00:14:56.750 - giving that massive acceleration that it offers, but 393 00:14:56.750 --> 00:14:59.230 - working alongside the the tools and expertise already 394 00:14:59.230 --> 00:14:59.715 - in place. 395 00:15:01.715 --> 00:15:04.434 - I see. And and may I suppose moving 396 00:15:04.434 --> 00:15:06.375 - on to AI, PhysicsX develops 397 00:15:07.394 --> 00:15:09.495 - large physics models for aerodynamics 398 00:15:10.115 --> 00:15:11.335 - and other applications. 399 00:15:11.809 --> 00:15:14.610 - What is a a large physics model? And 400 00:15:14.610 --> 00:15:16.309 - how does it differ from 401 00:15:16.690 --> 00:15:17.190 - conventional 402 00:15:17.649 --> 00:15:19.669 - computational approaches to aerodynamics? 403 00:15:21.490 --> 00:15:23.169 - Yeah. So so if I I kind of 404 00:15:23.169 --> 00:15:24.389 - start with the differences. 405 00:15:25.834 --> 00:15:26.334 - So 406 00:15:26.634 --> 00:15:29.274 - machine learning physics model's different in a in 407 00:15:29.274 --> 00:15:30.575 - a couple of different ways. 408 00:15:31.115 --> 00:15:32.654 - Unlike traditional computational 409 00:15:33.434 --> 00:15:33.934 - approaches, 410 00:15:34.315 --> 00:15:35.454 - machine learning model, 411 00:15:35.834 --> 00:15:37.774 - it it doesn't need to be told explicitly 412 00:15:37.914 --> 00:15:39.120 - what the laws of physics 413 00:15:39.600 --> 00:15:40.100 - are. 414 00:15:40.639 --> 00:15:42.879 - It learns it learns that from the data 415 00:15:42.879 --> 00:15:44.799 - it sees and and, you know, we we 416 00:15:44.799 --> 00:15:47.519 - use architectures that are suited to specific physics 417 00:15:47.519 --> 00:15:48.019 - problems. 418 00:15:48.559 --> 00:15:50.320 - And and and in doing that, it becomes 419 00:15:50.320 --> 00:15:53.174 - much more flexible than a traditional CFD tool. 420 00:15:53.975 --> 00:15:56.134 - It it doesn't require the same geometry preparation 421 00:15:56.134 --> 00:15:57.274 - we talked about earlier, 422 00:15:57.654 --> 00:15:59.654 - and and it can learn not just from 423 00:15:59.654 --> 00:16:02.534 - simulation data, but also from experimental data. So 424 00:16:02.534 --> 00:16:04.235 - it can actually bridge the gap. 425 00:16:06.160 --> 00:16:07.379 - It it can bridge the 426 00:16:07.840 --> 00:16:10.399 - gap, from from simulation world to kind of 427 00:16:10.399 --> 00:16:11.139 - ground truth. 428 00:16:12.160 --> 00:16:14.399 - It it it's got some obvious benefits in 429 00:16:14.399 --> 00:16:16.960 - terms of speed. Obviously, that that that's kind 430 00:16:16.960 --> 00:16:19.040 - of well known, and and and those speeds 431 00:16:19.040 --> 00:16:21.315 - can be up to, you know, 10,000 or 432 00:16:21.315 --> 00:16:23.735 - more times. So so really game changing. 433 00:16:24.434 --> 00:16:26.115 - And and that allows you to, as I 434 00:16:26.115 --> 00:16:26.615 - mentioned 435 00:16:27.235 --> 00:16:29.875 - in the example before, that really transforms what 436 00:16:29.875 --> 00:16:30.615 - you can do, 437 00:16:31.394 --> 00:16:34.274 - particularly when you get into solving multi physics 438 00:16:34.274 --> 00:16:35.334 - systems. So, 439 00:16:35.909 --> 00:16:36.970 - in in a traditional, 440 00:16:38.070 --> 00:16:39.909 - maybe in an OEM, you you you might 441 00:16:39.909 --> 00:16:41.049 - be solving problems 442 00:16:41.509 --> 00:16:43.529 - that that span across aerodynamics. 443 00:16:44.629 --> 00:16:46.309 - You've made it be in an engine design 444 00:16:46.309 --> 00:16:49.029 - where you've got aerodynamics involved, but you've also 445 00:16:49.029 --> 00:16:50.254 - got structures involved. 446 00:16:50.875 --> 00:16:53.514 - And and solving those different physics goes across 447 00:16:53.514 --> 00:16:55.934 - many groups and many groups of experts. 448 00:16:56.634 --> 00:16:58.095 - But with with these models, 449 00:16:59.035 --> 00:17:01.434 - then then you can solve those physics all 450 00:17:01.434 --> 00:17:03.660 - at the same time. And because the models 451 00:17:03.660 --> 00:17:05.440 - are more accessible than, 452 00:17:05.980 --> 00:17:07.599 - than than sort of simulation 453 00:17:08.140 --> 00:17:08.640 - tools, 454 00:17:08.940 --> 00:17:10.380 - then you can put them direct in the 455 00:17:10.380 --> 00:17:11.339 - hands of a designer. 456 00:17:11.660 --> 00:17:13.900 - And and that really starts to, again, change 457 00:17:13.900 --> 00:17:16.140 - the way you're working rather than just doing 458 00:17:16.140 --> 00:17:18.365 - things a bit faster. You you really start 459 00:17:18.365 --> 00:17:20.224 - to rethink how you're working. 460 00:17:20.764 --> 00:17:22.845 - And and then finally, another aspect of these 461 00:17:22.845 --> 00:17:24.144 - models is, 462 00:17:24.764 --> 00:17:26.845 - that that they've they've got this concept of 463 00:17:26.845 --> 00:17:27.345 - uncertainty 464 00:17:27.724 --> 00:17:28.224 - quantification. 465 00:17:28.605 --> 00:17:31.105 - So they they know how confident they are, 466 00:17:32.069 --> 00:17:32.569 - and 467 00:17:32.869 --> 00:17:34.549 - and and that's not just important from an 468 00:17:34.549 --> 00:17:36.390 - engineer's point of view because we need to 469 00:17:36.390 --> 00:17:37.450 - be building trust 470 00:17:37.750 --> 00:17:38.490 - in in, 471 00:17:39.029 --> 00:17:40.549 - in in the kind of use of of 472 00:17:40.549 --> 00:17:41.210 - the results. 473 00:17:42.230 --> 00:17:44.549 - But also from from the model point of 474 00:17:44.549 --> 00:17:46.890 - view, we can use that uncertainty to drive 475 00:17:47.204 --> 00:17:49.684 - the the simulations that the model needs to 476 00:17:49.684 --> 00:17:51.605 - be accurate in the places that it needs 477 00:17:51.605 --> 00:17:53.125 - to be. So we can have these, what 478 00:17:53.125 --> 00:17:54.664 - we call, active learning loops, 479 00:17:55.444 --> 00:17:58.585 - that that automatically trigger new set new simulations 480 00:17:58.724 --> 00:18:00.505 - when when a model becomes unsure. 481 00:18:03.279 --> 00:18:04.559 - And David, you, 482 00:18:04.880 --> 00:18:06.579 - and your colleagues at PhysicsX 483 00:18:06.880 --> 00:18:10.819 - have released a study called scaling physics AI 484 00:18:11.119 --> 00:18:11.940 - for automotive 485 00:18:12.319 --> 00:18:12.819 - aerodynamics. 486 00:18:13.759 --> 00:18:16.579 - And it argues that publicly available datasets 487 00:18:17.054 --> 00:18:17.875 - of automotive 488 00:18:18.254 --> 00:18:18.754 - simulations 489 00:18:19.534 --> 00:18:21.554 - are not sufficient to train 490 00:18:21.934 --> 00:18:23.474 - large physics models. 491 00:18:24.174 --> 00:18:26.274 - Why is this? And what are the consequences 492 00:18:26.494 --> 00:18:27.634 - for those using 493 00:18:28.095 --> 00:18:29.875 - AI to design cars? 494 00:18:31.390 --> 00:18:33.869 - Yeah. So so I should maybe give one 495 00:18:33.869 --> 00:18:36.589 - last piece of detail about the the these 496 00:18:36.589 --> 00:18:38.829 - types of models and and just to sort 497 00:18:38.829 --> 00:18:41.789 - of transition to the automotive large physics model 498 00:18:41.789 --> 00:18:42.450 - that we've, 499 00:18:43.069 --> 00:18:44.990 - developed at PhysicsX because I think that gives 500 00:18:44.990 --> 00:18:45.730 - some context. 501 00:18:46.855 --> 00:18:49.414 - And, you know, this is a pretrained model, 502 00:18:49.414 --> 00:18:51.335 - so it's already seen a huge amount of 503 00:18:51.335 --> 00:18:53.335 - data. And and that means it's something that 504 00:18:53.335 --> 00:18:55.335 - we can take to a customer that's that's 505 00:18:55.335 --> 00:18:56.535 - sort of ready to go out of the 506 00:18:56.535 --> 00:18:58.714 - box and and can predict aerodynamics 507 00:18:59.130 --> 00:19:00.829 - of previously unseen vehicles. 508 00:19:02.170 --> 00:19:04.329 - And and and a a really powerful aspect 509 00:19:04.329 --> 00:19:06.570 - of that is when we start fine tuning 510 00:19:06.570 --> 00:19:07.930 - that model. So we take the model that 511 00:19:07.930 --> 00:19:09.849 - we've developed in the house. We we fine 512 00:19:09.849 --> 00:19:12.944 - tune it for a given customer's needs or, 513 00:19:13.585 --> 00:19:17.044 - specifics of their car type and simulation approach. 514 00:19:17.505 --> 00:19:20.464 - But underpinning all of that is is is 515 00:19:20.464 --> 00:19:22.784 - the data, the high quality data that we 516 00:19:22.784 --> 00:19:25.024 - need to build that model. And and that 517 00:19:25.024 --> 00:19:26.404 - really comes to your question 518 00:19:28.430 --> 00:19:28.930 - about 519 00:19:30.670 --> 00:19:31.809 - the the need for, 520 00:19:33.150 --> 00:19:34.930 - the the the need for a dataset, 521 00:19:35.950 --> 00:19:36.450 - and 522 00:19:36.830 --> 00:19:37.330 - our, 523 00:19:39.150 --> 00:19:41.070 - our own our own dataset. So there's a 524 00:19:41.070 --> 00:19:42.934 - there's a couple of well publicized, 525 00:19:44.115 --> 00:19:47.394 - publicly available datasets, driver net and luminary shift 526 00:19:47.394 --> 00:19:47.894 - SUV. 527 00:19:49.075 --> 00:19:50.454 - And those were, 528 00:19:51.154 --> 00:19:53.335 - both derived from sort of parametric 529 00:19:53.794 --> 00:19:54.294 - morphing 530 00:19:54.994 --> 00:19:57.255 - of of a small number of base configurations. 531 00:19:58.180 --> 00:19:59.940 - And that that morphing is is a kind 532 00:19:59.940 --> 00:20:02.019 - of where you take a geometric mesh and 533 00:20:02.019 --> 00:20:03.400 - you kind of push and pull, 534 00:20:03.859 --> 00:20:05.640 - to to give geometric variation. 535 00:20:07.140 --> 00:20:08.840 - And, you know, these, 536 00:20:10.580 --> 00:20:11.960 - the these these variations, 537 00:20:13.195 --> 00:20:16.015 - obviously give give some diversity, but what we, 538 00:20:16.075 --> 00:20:17.515 - you know, what we saw when we we 539 00:20:17.515 --> 00:20:19.055 - started digging into these models 540 00:20:19.434 --> 00:20:22.075 - was that they performed well on on when 541 00:20:22.075 --> 00:20:24.875 - we tested them on geometries, which were very 542 00:20:24.875 --> 00:20:26.015 - similar to those, 543 00:20:26.795 --> 00:20:27.295 - datasets. 544 00:20:27.929 --> 00:20:29.769 - But but really, they started to fail quite 545 00:20:29.769 --> 00:20:31.869 - badly when we looked at realistic 546 00:20:32.569 --> 00:20:35.210 - customer geometries, which which were of a different 547 00:20:35.210 --> 00:20:35.710 - design. 548 00:20:38.730 --> 00:20:41.794 - So to to address this shortcoming that that 549 00:20:41.794 --> 00:20:43.815 - you've identified in your study, 550 00:20:44.115 --> 00:20:47.734 - PhysicsX has created the p x NETCAR 551 00:20:48.355 --> 00:20:48.855 - proprietary 552 00:20:49.315 --> 00:20:49.815 - dataset. 553 00:20:50.835 --> 00:20:52.914 - How did you develop this, and how does 554 00:20:52.914 --> 00:20:54.775 - it compare to public datasets? 555 00:20:56.670 --> 00:20:57.390 - Yeah. So, 556 00:20:57.950 --> 00:21:00.269 - we we kind of having having drawn that 557 00:21:00.269 --> 00:21:02.690 - conclusion that that, you know, we we needed 558 00:21:02.910 --> 00:21:03.410 - this 559 00:21:04.029 --> 00:21:05.809 - more diversity, so we built, 560 00:21:06.109 --> 00:21:07.009 - what we call, 561 00:21:07.470 --> 00:21:09.934 - the the Physics x Data Factory, and that's 562 00:21:09.934 --> 00:21:11.315 - really an automated pipeline 563 00:21:11.855 --> 00:21:13.474 - that can run many, many, 564 00:21:14.255 --> 00:21:14.755 - simulations. 565 00:21:15.134 --> 00:21:18.035 - In this case, STAR CCM plus runs, 566 00:21:18.414 --> 00:21:21.075 - Reynolds' average average Navios Stokes simulations. 567 00:21:22.589 --> 00:21:24.349 - So, you know, we we got a hold 568 00:21:24.349 --> 00:21:25.730 - of 450 569 00:21:25.789 --> 00:21:27.410 - quite distinct car shapes, 570 00:21:28.109 --> 00:21:30.450 - from large scale three d model libraries. 571 00:21:31.230 --> 00:21:32.289 - And then we 572 00:21:32.910 --> 00:21:35.044 - produced our own kinda algorithmic 573 00:21:35.424 --> 00:21:37.605 - morphing approach so that we could create 574 00:21:38.065 --> 00:21:40.384 - around a 100 variants for for each of 575 00:21:40.384 --> 00:21:43.204 - these baselines using a a tool called Blender, 576 00:21:43.825 --> 00:21:45.825 - which is an open source three d modeling 577 00:21:45.825 --> 00:21:46.325 - tool. 578 00:21:47.009 --> 00:21:49.109 - And and we used a Latin hypertube 579 00:21:49.730 --> 00:21:51.970 - sampling approach so that we knew we were 580 00:21:51.970 --> 00:21:54.150 - kind of creating a a statistically, 581 00:21:55.890 --> 00:21:57.990 - good coverage of of these geometries. 582 00:21:59.105 --> 00:22:01.845 - And then we we rigorously validated, 583 00:22:02.705 --> 00:22:04.144 - those results in terms of the kind of 584 00:22:04.144 --> 00:22:06.945 - convergence and the correctness of the physics knowing 585 00:22:06.945 --> 00:22:07.924 - that, you know, 586 00:22:08.384 --> 00:22:10.484 - the the the quality of that data was 587 00:22:10.545 --> 00:22:12.865 - critical to the performance of the model at 588 00:22:12.865 --> 00:22:13.525 - the end. 589 00:22:13.970 --> 00:22:16.710 - So that that allowed us to build PXnetcar, 590 00:22:17.809 --> 00:22:18.869 - which now comprises 591 00:22:19.170 --> 00:22:20.869 - over 30,000 simulations 592 00:22:21.410 --> 00:22:23.250 - across those 450 593 00:22:23.250 --> 00:22:23.750 - distinct, 594 00:22:24.609 --> 00:22:26.434 - geometries. And, you know, 595 00:22:26.894 --> 00:22:29.554 - comparing back to the driver net and luminary 596 00:22:30.014 --> 00:22:31.774 - shift, which is where we started with, you 597 00:22:31.774 --> 00:22:32.355 - know, between 598 00:22:33.214 --> 00:22:35.714 - those two, that they they gave us 10, 599 00:22:36.494 --> 00:22:39.375 - kind of 10 distinct geometry start points. So 600 00:22:39.375 --> 00:22:41.554 - it's it's really a complete game changer 601 00:22:41.910 --> 00:22:43.289 - scaling up that much. 602 00:22:43.990 --> 00:22:45.830 - And and, you know, we wanted to be 603 00:22:45.830 --> 00:22:46.890 - quite quantitative 604 00:22:47.269 --> 00:22:50.390 - about the, the diversity we were generating. So 605 00:22:50.390 --> 00:22:52.730 - we use something called pairwise chamfer distance, 606 00:22:53.509 --> 00:22:56.170 - which basically measures between different geometries 607 00:22:56.605 --> 00:22:58.284 - the the the kind of distance between all 608 00:22:58.284 --> 00:23:00.365 - the points, and and it gives a a 609 00:23:00.365 --> 00:23:01.184 - way of quantifying 610 00:23:01.565 --> 00:23:04.365 - that diversity. And we could see as we 611 00:23:04.365 --> 00:23:06.444 - build up that dataset that we were, 612 00:23:07.325 --> 00:23:09.325 - that we were really building a a very 613 00:23:09.325 --> 00:23:09.825 - diverse, 614 00:23:10.970 --> 00:23:12.809 - set of classes, not just sort of minor 615 00:23:12.809 --> 00:23:13.309 - morphs. 616 00:23:14.009 --> 00:23:16.269 - And then when we started applying that, 617 00:23:16.890 --> 00:23:19.529 - a large physics model in in practice, then 618 00:23:19.529 --> 00:23:22.750 - that very clearly translated into much greater generalization 619 00:23:23.210 --> 00:23:25.115 - performance. So, you you know, when we started 620 00:23:25.174 --> 00:23:27.355 - showing it new geometries, new cars, 621 00:23:28.214 --> 00:23:31.015 - that then the performance was much much higher 622 00:23:31.015 --> 00:23:33.095 - than than than the models that we'd, 623 00:23:33.494 --> 00:23:35.355 - trained on those narrower, luminary, 624 00:23:36.375 --> 00:23:38.315 - and, and and driver net datasets. 625 00:23:40.410 --> 00:23:42.970 - And and we've been talking about cars, David, 626 00:23:42.970 --> 00:23:46.349 - but PhysicsX has a range of interests outside 627 00:23:46.410 --> 00:23:49.130 - of vehicle design. Can you talk about some 628 00:23:49.130 --> 00:23:51.390 - of the other sectors that you're developing 629 00:23:52.154 --> 00:23:53.215 - AI tools for? 630 00:23:54.075 --> 00:23:55.355 - Yeah. Absolutely. Yeah. 631 00:23:55.914 --> 00:23:58.075 - I mean, we work on some incredibly exciting 632 00:23:58.075 --> 00:23:58.575 - projects. 633 00:23:59.195 --> 00:24:00.955 - And, you know, many of these we can't 634 00:24:00.955 --> 00:24:03.055 - talk about because they're they're customer projects. 635 00:24:03.355 --> 00:24:05.035 - I happen to be working on one which 636 00:24:05.035 --> 00:24:07.579 - is, has been publicly announced, so that that's 637 00:24:07.579 --> 00:24:08.319 - quite helpful. 638 00:24:08.859 --> 00:24:10.940 - But starting more generally, you know, our our 639 00:24:10.940 --> 00:24:13.519 - models add most value in in in sectors 640 00:24:13.579 --> 00:24:14.079 - where 641 00:24:14.380 --> 00:24:15.679 - complex physics simulation 642 00:24:15.980 --> 00:24:18.460 - is right at the heart of engineering decision 643 00:24:18.460 --> 00:24:20.914 - making or operation. And, generally, you know, the 644 00:24:20.914 --> 00:24:22.595 - time that we spoke about earlier to solve 645 00:24:22.595 --> 00:24:23.335 - those simulations 646 00:24:23.795 --> 00:24:25.974 - can be a real bottleneck for for engineers 647 00:24:26.035 --> 00:24:27.174 - or product development. 648 00:24:28.115 --> 00:24:30.615 - Commonly, we see this across aerospace and semiconductors, 649 00:24:31.154 --> 00:24:33.240 - energy, and materials. So, you know, this is 650 00:24:33.240 --> 00:24:35.099 - where we we're doing a lot of work. 651 00:24:35.799 --> 00:24:38.119 - And and, also, it's not just about design, 652 00:24:38.119 --> 00:24:40.119 - but because these models can solve more or 653 00:24:40.119 --> 00:24:43.339 - less real time, they become ideal for manufacturing 654 00:24:43.720 --> 00:24:45.255 - and and operation processes. 655 00:24:46.134 --> 00:24:46.634 - You 656 00:24:47.015 --> 00:24:48.615 - know, just to give a small sense of 657 00:24:48.615 --> 00:24:50.394 - the complexity, a recent project 658 00:24:50.855 --> 00:24:53.494 - we developed was a a plasma reactor for 659 00:24:53.494 --> 00:24:57.515 - for converting industrial waste gases into synthetic fuels. 660 00:24:57.619 --> 00:25:00.340 - So basically taking, gases including c o two 661 00:25:00.340 --> 00:25:02.680 - and then converting that into carbon monoxide, 662 00:25:03.779 --> 00:25:05.779 - which which then went on into these, 663 00:25:07.299 --> 00:25:08.519 - synthetic hydrocarbons. 664 00:25:09.924 --> 00:25:11.605 - And, you know, we had to model all 665 00:25:11.605 --> 00:25:14.085 - the chemical reactions, the mixing of flow through 666 00:25:14.085 --> 00:25:14.744 - the reactor, 667 00:25:15.684 --> 00:25:17.845 - and and the thermal modeling, of course, to 668 00:25:17.845 --> 00:25:19.924 - to make sure that kind of critical components 669 00:25:19.924 --> 00:25:20.664 - didn't overheat. 670 00:25:21.285 --> 00:25:23.045 - And with with this technology, we can do 671 00:25:23.045 --> 00:25:24.025 - all of that simultaneously. 672 00:25:24.565 --> 00:25:25.140 - So it's 673 00:25:25.619 --> 00:25:27.160 - massive, massive speed up. 674 00:25:27.779 --> 00:25:29.940 - And, you know, we we we reduced the 675 00:25:29.940 --> 00:25:32.019 - r and d process by a couple of 676 00:25:32.019 --> 00:25:34.259 - years, and and in the end, had a 677 00:25:34.259 --> 00:25:36.820 - machine that was giving a tenfold increase in 678 00:25:36.820 --> 00:25:39.779 - react productivity, which is, you know, an absolute 679 00:25:39.779 --> 00:25:40.424 - game changer. 680 00:25:41.705 --> 00:25:43.545 - Moving moving on to the project that I'm 681 00:25:43.545 --> 00:25:44.365 - involved in, 682 00:25:45.785 --> 00:25:48.825 - so so this is working with GB one, 683 00:25:48.825 --> 00:25:51.305 - which is The UK's entrant to the twenty 684 00:25:51.305 --> 00:25:52.845 - seven America's Cup. 685 00:25:53.930 --> 00:25:55.930 - And it's I think it's an interesting example 686 00:25:55.930 --> 00:25:58.109 - because, again, it's it's a very advanced engineering 687 00:25:58.170 --> 00:26:00.809 - workflow. And and and for some context for 688 00:26:00.809 --> 00:26:02.029 - those maybe not aware, 689 00:26:02.650 --> 00:26:06.109 - America's cup boats are 75 foot hydrofoiling 690 00:26:06.490 --> 00:26:07.335 - sailing yachts, 691 00:26:08.054 --> 00:26:10.154 - often better described as flying machines. 692 00:26:10.454 --> 00:26:12.534 - You know, they they they go pretty crazy 693 00:26:12.534 --> 00:26:15.015 - speeds in excess of four and a half 694 00:26:15.015 --> 00:26:17.335 - times wind speed, which as an engineer takes 695 00:26:17.335 --> 00:26:19.034 - a bit of wrapping your head around, 696 00:26:19.849 --> 00:26:21.930 - which means they go they go above a 697 00:26:21.930 --> 00:26:23.470 - 100 kilometers an hour. 698 00:26:23.849 --> 00:26:25.450 - And I was lucky to join the team, 699 00:26:25.690 --> 00:26:27.369 - for some testing in Sardinia a couple of 700 00:26:27.369 --> 00:26:29.450 - weeks ago. And and then you really do 701 00:26:29.450 --> 00:26:31.049 - get a sense of of the speed when 702 00:26:31.049 --> 00:26:32.890 - this boat is is moving towards you at 703 00:26:32.890 --> 00:26:34.269 - that that kind of speed. 704 00:26:35.525 --> 00:26:37.525 - And and, you know, like in f one, 705 00:26:37.525 --> 00:26:40.244 - it's very much a competition between engine engineering 706 00:26:40.244 --> 00:26:40.744 - departments. 707 00:26:41.924 --> 00:26:44.904 - So they've got very, very refined development processes. 708 00:26:45.365 --> 00:26:47.444 - But one of the big remaining challenges is 709 00:26:47.444 --> 00:26:48.825 - just a massive dimensionality, 710 00:26:50.460 --> 00:26:52.539 - of of the design and operating space that 711 00:26:52.539 --> 00:26:54.700 - that the engineers have to contend with. And 712 00:26:54.700 --> 00:26:57.500 - this is where boats are different from f 713 00:26:57.500 --> 00:26:58.160 - one cars. 714 00:26:59.339 --> 00:27:00.940 - You know, f one cars, you've got the 715 00:27:00.940 --> 00:27:03.519 - steering and and accelerator brake pedal as input. 716 00:27:03.740 --> 00:27:06.945 - But with the boats, you've got multiple controls 717 00:27:06.945 --> 00:27:08.005 - across the foils, 718 00:27:08.384 --> 00:27:09.045 - the sails, 719 00:27:09.825 --> 00:27:12.065 - you know, the the sail shape itself, you've 720 00:27:12.065 --> 00:27:14.305 - got many controls, plus you've got the the 721 00:27:14.305 --> 00:27:15.045 - wind effect. 722 00:27:15.664 --> 00:27:17.605 - So not only are there all these design 723 00:27:18.160 --> 00:27:20.160 - parameters, but there's also all these operational ones 724 00:27:20.160 --> 00:27:22.240 - as well. And and that's kind of where 725 00:27:22.240 --> 00:27:22.640 - we've found, 726 00:27:24.319 --> 00:27:25.920 - you know, a a a a really good 727 00:27:25.920 --> 00:27:28.579 - fit between GP one and PhysicsX because 728 00:27:29.039 --> 00:27:29.539 - our, 729 00:27:30.079 --> 00:27:30.819 - kind of 730 00:27:31.119 --> 00:27:32.414 - physics aware architecture 731 00:27:32.894 --> 00:27:35.634 - can learn all those complex interactions, aerodynamics, 732 00:27:36.015 --> 00:27:36.515 - hydrodynamics, 733 00:27:38.494 --> 00:27:40.515 - and and predict near real time 734 00:27:40.894 --> 00:27:42.654 - both the flow around the car, but also 735 00:27:42.654 --> 00:27:43.154 - the, 736 00:27:43.775 --> 00:27:45.839 - the the the loads the loads on the 737 00:27:46.079 --> 00:27:48.019 - sorry. Around the boat, I should say. 738 00:27:48.799 --> 00:27:50.960 - And also the the the the loads that 739 00:27:50.960 --> 00:27:53.599 - that then feed into GB one simulation so 740 00:27:53.599 --> 00:27:55.200 - we can get a a a kind of 741 00:27:55.200 --> 00:27:56.259 - performance output. 742 00:27:57.200 --> 00:27:58.880 - And and and also in, you know, in 743 00:27:58.880 --> 00:28:00.615 - the structural domain as well. So 744 00:28:01.494 --> 00:28:03.355 - we we can give a really kinda complete 745 00:28:03.575 --> 00:28:04.075 - optimization. 746 00:28:05.974 --> 00:28:07.434 - So, you know, that that's, 747 00:28:07.974 --> 00:28:10.134 - using our platform, taking the learning from the 748 00:28:10.134 --> 00:28:12.454 - research group, the, you know, the the the 749 00:28:12.454 --> 00:28:12.954 - LPM 750 00:28:14.099 --> 00:28:16.200 - development that we spoke about, we can directly 751 00:28:16.259 --> 00:28:19.140 - translate very quickly into something that's been useful 752 00:28:19.140 --> 00:28:20.519 - for the GB one team. 753 00:28:21.859 --> 00:28:22.359 - And, 754 00:28:22.980 --> 00:28:24.579 - you know, in in in doing that, we 755 00:28:24.660 --> 00:28:27.299 - we're building very much on their expertise and 756 00:28:27.299 --> 00:28:28.679 - their physical insight. 757 00:28:29.555 --> 00:28:31.335 - But with with such a vast, 758 00:28:31.634 --> 00:28:33.414 - you know, vast space to explore, 759 00:28:33.875 --> 00:28:36.055 - the these tools are a complete game changer. 760 00:28:37.154 --> 00:28:37.555 - And, 761 00:28:38.275 --> 00:28:38.934 - you know, 762 00:28:39.875 --> 00:28:41.315 - one one of one of the points we 763 00:28:41.315 --> 00:28:43.789 - touched on earlier was, you know, bound the 764 00:28:43.789 --> 00:28:45.789 - the confidence of these models. And when we 765 00:28:45.789 --> 00:28:47.410 - want to start exploring such, 766 00:28:48.430 --> 00:28:49.170 - fast spaces, 767 00:28:49.549 --> 00:28:51.650 - we can, again, we can use that uncertainty 768 00:28:51.869 --> 00:28:54.349 - to kind of drive where we collect data. 769 00:28:54.349 --> 00:28:57.835 - So we we we're starting relatively small with 770 00:28:57.835 --> 00:28:58.494 - the dataset, 771 00:28:59.195 --> 00:29:01.835 - and and then as we start optimizing and 772 00:29:01.835 --> 00:29:04.555 - the optimizer is exploring the design space, we 773 00:29:04.555 --> 00:29:06.174 - can collect more and more data. 774 00:29:06.555 --> 00:29:06.955 - So, 775 00:29:07.355 --> 00:29:09.269 - you know, what we're seeing is is all 776 00:29:09.269 --> 00:29:10.549 - of the all of the, 777 00:29:11.190 --> 00:29:12.869 - lessons that we've learned in the, 778 00:29:13.590 --> 00:29:16.309 - large physics model are are giving us this 779 00:29:16.309 --> 00:29:18.549 - leg up with with our GPU one work, 780 00:29:18.549 --> 00:29:20.090 - and that's that's really exciting. 781 00:29:21.174 --> 00:29:22.934 - When when you're doing the, 782 00:29:23.974 --> 00:29:25.575 - the the work, you know, with the with 783 00:29:25.575 --> 00:29:26.234 - the boat, 784 00:29:26.615 --> 00:29:29.515 - are you are you just looking at 785 00:29:30.775 --> 00:29:31.275 - design, 786 00:29:31.575 --> 00:29:33.195 - or are you also 787 00:29:33.734 --> 00:29:34.529 - looking at 788 00:29:36.210 --> 00:29:38.609 - the the way in which the boat can 789 00:29:38.609 --> 00:29:39.349 - be sailed? 790 00:29:39.890 --> 00:29:41.190 - You know, looking for, 791 00:29:42.210 --> 00:29:44.210 - I don't know, maybe different ways of doing 792 00:29:44.210 --> 00:29:47.090 - things or the best way of of sailing 793 00:29:47.090 --> 00:29:48.630 - the boat in addition 794 00:29:49.154 --> 00:29:49.654 - to, 795 00:29:51.555 --> 00:29:52.695 - I suppose, adjust, 796 00:29:53.075 --> 00:29:55.075 - for lack of a better term, the the 797 00:29:55.075 --> 00:29:55.894 - fluid dynamics 798 00:29:56.515 --> 00:29:58.835 - of the boat? Or is that is is 799 00:29:58.835 --> 00:30:00.994 - that something different or maybe something that you 800 00:30:00.994 --> 00:30:02.660 - could look at in the future? 801 00:30:03.440 --> 00:30:05.759 - Well, yeah. Great question. And and maybe there's 802 00:30:05.759 --> 00:30:07.299 - a bit of both there because, 803 00:30:07.680 --> 00:30:09.440 - you know, even at the design stage, if 804 00:30:09.440 --> 00:30:11.279 - we wanna optimize the design, we need to 805 00:30:11.279 --> 00:30:13.220 - consider how the boat's gonna be sailed. 806 00:30:13.855 --> 00:30:15.134 - You know, we we can't do it in 807 00:30:15.134 --> 00:30:17.134 - isolation if we want to really kind of 808 00:30:17.134 --> 00:30:19.774 - get to a optimum performance. So, you know, 809 00:30:19.774 --> 00:30:20.755 - we are considering, 810 00:30:22.414 --> 00:30:25.394 - many aspects of how the boat will sail, 811 00:30:25.615 --> 00:30:27.375 - you know, as we're as we're going through 812 00:30:27.375 --> 00:30:28.434 - that design iteration. 813 00:30:30.869 --> 00:30:32.950 - But to your, you know, to your point, 814 00:30:32.950 --> 00:30:34.710 - we we can also use these types of 815 00:30:34.710 --> 00:30:37.049 - models in operations. So we're we're we're absolutely 816 00:30:37.190 --> 00:30:39.269 - thinking about, you know, how we might be 817 00:30:39.269 --> 00:30:41.684 - able to, kind of take the next step 818 00:30:41.684 --> 00:30:42.184 - and, 819 00:30:42.964 --> 00:30:45.144 - deploy these models, whether that's sort of, 820 00:30:46.565 --> 00:30:48.644 - give it giving kind of an almost advice 821 00:30:48.644 --> 00:30:50.664 - to the sailors on on on optimum, 822 00:30:51.285 --> 00:30:54.085 - sailing strategies or or or in in in 823 00:30:54.085 --> 00:30:54.585 - control. 824 00:30:57.200 --> 00:30:59.920 - And finally, here at Physics World, we're always 825 00:30:59.920 --> 00:31:01.619 - really interested in exploring 826 00:31:02.240 --> 00:31:02.740 - careers 827 00:31:03.200 --> 00:31:04.019 - for physicists. 828 00:31:04.720 --> 00:31:06.820 - And, I'm sure that some of our listeners 829 00:31:07.519 --> 00:31:09.914 - would be interested in a career in the 830 00:31:09.914 --> 00:31:12.815 - commercial development of large physics models. 831 00:31:13.355 --> 00:31:14.654 - What sort of employment 832 00:31:15.115 --> 00:31:17.134 - prospects are there in the sector? 833 00:31:17.595 --> 00:31:19.674 - And could could you maybe give a a 834 00:31:19.674 --> 00:31:22.875 - bit of career advice for someone who who'd 835 00:31:22.875 --> 00:31:24.495 - like to to get a job, 836 00:31:25.529 --> 00:31:26.750 - developing these models? 837 00:31:28.089 --> 00:31:31.450 - Yeah. Absolutely. And, you know, my own journey 838 00:31:31.450 --> 00:31:33.690 - to physics x is is is maybe not 839 00:31:33.690 --> 00:31:34.669 - a typical one. 840 00:31:36.329 --> 00:31:38.565 - But but maybe maybe there's a little bit, 841 00:31:38.644 --> 00:31:40.484 - you know, in in that journey that I 842 00:31:40.484 --> 00:31:42.105 - I still sort of take away. 843 00:31:42.804 --> 00:31:43.544 - You know, 844 00:31:44.004 --> 00:31:45.365 - when when I started out in f one, 845 00:31:45.365 --> 00:31:47.125 - it was definitely not a well trodden path. 846 00:31:47.125 --> 00:31:48.484 - And, you know, I was writing a lot 847 00:31:48.484 --> 00:31:50.484 - of letters, and I was looking for any 848 00:31:50.484 --> 00:31:51.539 - break I could get. 849 00:31:52.019 --> 00:31:53.859 - And and and, actually, my break came from 850 00:31:53.859 --> 00:31:56.180 - a rejection from what was then British American 851 00:31:56.180 --> 00:31:58.420 - Racing. You know, they wanted to employ some 852 00:31:58.420 --> 00:32:00.180 - of the experience, but they, 853 00:32:01.059 --> 00:32:03.059 - they they were on a notice period. So 854 00:32:03.059 --> 00:32:05.059 - there was, there there was a a a 855 00:32:05.059 --> 00:32:06.740 - few months where there was a vacant seat. 856 00:32:06.740 --> 00:32:07.515 - And so I 857 00:32:07.994 --> 00:32:10.075 - said, okay. Can I can I just come 858 00:32:10.075 --> 00:32:12.154 - and, you know, sit there and and learn 859 00:32:12.154 --> 00:32:13.914 - for a bit? And and and, actually, I 860 00:32:13.914 --> 00:32:15.674 - had the chance while I was there to 861 00:32:15.674 --> 00:32:18.015 - go and do some, testing in Italy. 862 00:32:18.795 --> 00:32:19.755 - We we were, 863 00:32:20.559 --> 00:32:23.599 - basically kind of putting wires across the the 864 00:32:23.599 --> 00:32:26.480 - race car to understand deflection effects. And then 865 00:32:26.480 --> 00:32:28.080 - I learned a huge amount. It was a 866 00:32:28.080 --> 00:32:30.240 - a great education. And, you know, it was 867 00:32:30.240 --> 00:32:32.755 - a a lesson definitely for me in, you 868 00:32:32.755 --> 00:32:35.154 - know, looking for those opportunities even when they 869 00:32:35.154 --> 00:32:36.855 - come in in unexpected ways. 870 00:32:38.034 --> 00:32:39.335 - And, you know, that 871 00:32:39.875 --> 00:32:41.875 - gave me my career in f one and 872 00:32:41.875 --> 00:32:43.335 - and then the the opportunity, 873 00:32:44.275 --> 00:32:47.559 - and the connection with PhysicsX also came with 874 00:32:47.559 --> 00:32:48.299 - some serendipity. 875 00:32:49.320 --> 00:32:51.740 - You know, it wasn't an opportunity I anticipated, 876 00:32:51.880 --> 00:32:53.559 - but, you know, after my experience as a 877 00:32:53.559 --> 00:32:55.980 - customer, not just the the impact 878 00:32:56.279 --> 00:32:57.559 - that I saw in f one, but the 879 00:32:57.559 --> 00:32:59.660 - scalability of this of a wider industry. 880 00:33:00.025 --> 00:33:01.464 - You know, I I knew it would be 881 00:33:01.464 --> 00:33:03.944 - an amazing place to be. And so, again, 882 00:33:03.944 --> 00:33:05.484 - kind of looking for that opportunity 883 00:33:06.025 --> 00:33:07.804 - to to kind of make the connections. 884 00:33:09.545 --> 00:33:10.744 - And now, you know, now I am at 885 00:33:10.744 --> 00:33:12.744 - PhysicsX, and I'm I'm seeing, you know, the 886 00:33:12.744 --> 00:33:14.744 - work that we're doing on on large physics 887 00:33:14.744 --> 00:33:15.244 - models. 888 00:33:15.730 --> 00:33:17.269 - You know, it's incredibly multidisciplinary. 889 00:33:19.009 --> 00:33:19.509 - Obviously, 890 00:33:19.890 --> 00:33:21.269 - it needs domain expertise. 891 00:33:22.769 --> 00:33:25.509 - Also, you know, we we need simulation engineering 892 00:33:25.569 --> 00:33:27.970 - to generate and validate our our datasets. We 893 00:33:27.970 --> 00:33:29.875 - talked about how important they are. And the 894 00:33:29.875 --> 00:33:33.394 - machine learning research spanning areas like geometric learning, 895 00:33:33.394 --> 00:33:34.615 - physics informed methods, 896 00:33:35.394 --> 00:33:37.575 - and and a lot of statistics and uncertainty 897 00:33:37.634 --> 00:33:40.934 - quantification. So all, you know, really important ingredients. 898 00:33:41.554 --> 00:33:42.035 - And, 899 00:33:42.355 --> 00:33:43.095 - then familiarity 900 00:33:43.394 --> 00:33:43.894 - with 901 00:33:44.630 --> 00:33:47.190 - pretrained models is increasingly relevant, and that could 902 00:33:47.190 --> 00:33:50.409 - come from language or image or video. So, 903 00:33:50.710 --> 00:33:52.409 - you know, many of these 904 00:33:52.789 --> 00:33:54.089 - same ideas and architectures, 905 00:33:55.589 --> 00:33:57.750 - kind of transfer into this domain and and 906 00:33:57.750 --> 00:33:59.289 - and and so those experiences 907 00:33:59.994 --> 00:34:00.974 - are all relevant. 908 00:34:03.034 --> 00:34:04.394 - I I guess what I've seen is, 909 00:34:05.994 --> 00:34:07.214 - it's not just about 910 00:34:07.674 --> 00:34:09.514 - one one skill that sort of makes the 911 00:34:09.514 --> 00:34:12.315 - difference for us. It is the collaboration across 912 00:34:12.315 --> 00:34:15.614 - physicists and simulation engineers and machine learning researchers, 913 00:34:16.050 --> 00:34:17.190 - infrastructure engineers, 914 00:34:17.570 --> 00:34:18.469 - you know. And and 915 00:34:18.930 --> 00:34:20.949 - from my own experience, often, you 916 00:34:21.410 --> 00:34:23.450 - know, there there's a different language there eve 917 00:34:23.570 --> 00:34:25.250 - even for the for the same problem that 918 00:34:25.250 --> 00:34:27.489 - you're trying to solve. And so bridging the 919 00:34:27.489 --> 00:34:29.930 - gaps between all these different experts is where 920 00:34:29.930 --> 00:34:31.590 - a lot of the value is created. 921 00:34:32.264 --> 00:34:35.385 - And so, you know, I think, of course, 922 00:34:35.385 --> 00:34:37.545 - being an expert in one area is is 923 00:34:37.545 --> 00:34:39.944 - absolutely the foundation of your credibility, and there 924 00:34:39.944 --> 00:34:41.464 - are, you know, the areas that I've talked 925 00:34:41.464 --> 00:34:43.864 - about there. But, also, I would advise you 926 00:34:43.864 --> 00:34:44.364 - know, 927 00:34:45.150 --> 00:34:46.609 - try and develop that familiarity 928 00:34:46.909 --> 00:34:49.329 - and understanding across the full scope 929 00:34:49.710 --> 00:34:52.269 - because the people that can kind of really 930 00:34:52.269 --> 00:34:54.190 - thrive are those that can have a meaningful 931 00:34:54.190 --> 00:34:54.690 - conversation 932 00:34:54.989 --> 00:34:57.170 - both with a a maybe a CFD engineering 933 00:34:57.835 --> 00:34:59.914 - engineer in the morning or or a system 934 00:34:59.914 --> 00:35:01.755 - architect in the afternoon and and and and 935 00:35:01.755 --> 00:35:03.055 - join those dots up. 936 00:35:04.315 --> 00:35:04.815 - And 937 00:35:05.114 --> 00:35:06.394 - one of one of the things I've loved 938 00:35:06.394 --> 00:35:08.635 - about being at PhysicsX is we we hot 939 00:35:08.635 --> 00:35:10.394 - desk here. And, you know, every day I 940 00:35:10.394 --> 00:35:11.775 - find myself sitting someone, 941 00:35:12.119 --> 00:35:14.359 - sitting next to someone with a really deep 942 00:35:14.359 --> 00:35:14.859 - expertise 943 00:35:15.239 --> 00:35:17.739 - in in something fascinating that I've learned from. 944 00:35:18.199 --> 00:35:20.839 - So I suppose my overall advice there is, 945 00:35:20.839 --> 00:35:23.339 - you know, be curious and be open minded. 946 00:35:24.515 --> 00:35:26.114 - Yeah. You never know where where the next 947 00:35:26.114 --> 00:35:28.135 - opportunity will come from or the next idea. 948 00:35:28.994 --> 00:35:30.675 - And and and there's a good chance the 949 00:35:30.675 --> 00:35:33.394 - next best idea for you will come from 950 00:35:33.394 --> 00:35:36.515 - someone else. So, yeah, stay receptive to people 951 00:35:36.515 --> 00:35:37.574 - around you and, 952 00:35:38.820 --> 00:35:40.599 - you know, always ask for feedback, 953 00:35:41.300 --> 00:35:43.780 - because it's it's really the best gift that 954 00:35:43.780 --> 00:35:44.599 - you can get. 955 00:35:45.059 --> 00:35:46.420 - So I think that would be that would 956 00:35:46.420 --> 00:35:47.320 - be my advice. 957 00:35:48.099 --> 00:35:49.619 - Well, it sounds I mean, it sounds like 958 00:35:49.619 --> 00:35:50.920 - a fascinating field 959 00:35:51.234 --> 00:35:52.054 - that you're in. 960 00:35:52.914 --> 00:35:54.695 - And, you know, especially for, 961 00:35:55.074 --> 00:35:56.914 - you know, for for for a physicist or 962 00:35:56.914 --> 00:35:57.494 - a mathematician 963 00:35:57.875 --> 00:35:59.875 - or, I suppose, even with you, a a 964 00:35:59.875 --> 00:36:00.375 - chemist, 965 00:36:01.474 --> 00:36:03.474 - that, you you know, there that there are 966 00:36:03.474 --> 00:36:03.974 - opportunities. 967 00:36:05.059 --> 00:36:06.739 - You know, I suppose AI gets a a 968 00:36:06.739 --> 00:36:09.139 - lot of bad press these days, but, 969 00:36:09.860 --> 00:36:11.960 - it sounds like it's really opening up, 970 00:36:13.460 --> 00:36:14.820 - a a a whole new world when it 971 00:36:14.820 --> 00:36:15.460 - comes to, 972 00:36:16.099 --> 00:36:16.840 - to design. 973 00:36:17.824 --> 00:36:20.704 - Yeah. You you absolutely. And and and, you 974 00:36:20.704 --> 00:36:21.344 - know, I think, 975 00:36:21.904 --> 00:36:24.385 - the the AI in the physical space is, 976 00:36:24.385 --> 00:36:26.385 - you know, is is quite a different beast, 977 00:36:26.385 --> 00:36:28.644 - and it's it's really accelerating. 978 00:36:29.184 --> 00:36:31.500 - And as you mentioned, you know, we we 979 00:36:31.500 --> 00:36:32.000 - we 980 00:36:32.380 --> 00:36:35.019 - have experts in in chemistry as well as 981 00:36:35.019 --> 00:36:35.519 - physics, 982 00:36:36.460 --> 00:36:36.960 - and, 983 00:36:37.739 --> 00:36:40.800 - you know, bringing bringing bringing those experts together 984 00:36:40.860 --> 00:36:42.380 - as well as, of course, the the kind 985 00:36:42.380 --> 00:36:43.280 - of fundamental 986 00:36:45.425 --> 00:36:47.585 - AI behind the under the hood, you know, 987 00:36:47.585 --> 00:36:49.285 - that's kind of where the magic happens. 988 00:36:50.545 --> 00:36:52.704 - Well, that's great. Thanks, David. Thanks so much 989 00:36:52.704 --> 00:36:54.864 - for, for joining us today and, 990 00:36:55.344 --> 00:36:57.585 - talking about your career and your work at, 991 00:36:57.905 --> 00:37:00.320 - PhysicsX. It's been a pleasure. Thanks very much, 992 00:37:00.780 --> 00:37:01.280 - Hamish. 993 00:37:09.019 --> 00:37:10.860 - I'm afraid that's all the time we have 994 00:37:10.860 --> 00:37:12.160 - for this week's podcast. 995 00:37:12.635 --> 00:37:15.914 - Thanks to Dave Wheater of Physics x for 996 00:37:15.914 --> 00:37:17.295 - an enlightening discussion 997 00:37:17.675 --> 00:37:19.775 - of how large physics models 998 00:37:20.074 --> 00:37:21.375 - are making cars 999 00:37:21.675 --> 00:37:22.494 - more efficient. 1000 00:37:23.355 --> 00:37:26.554 - I'm Hamish Johnston, and our producer is Fred 1001 00:37:26.554 --> 00:37:27.054 - Isles. 1002 00:37:27.699 --> 00:37:30.260 - The music for this episode is called one 1003 00:37:30.260 --> 00:37:33.320 - three seven, and it was composed and performed 1004 00:37:33.619 --> 00:37:34.599 - by the physicist 1005 00:37:35.059 --> 00:37:36.119 - Philip Moriarty. 1006 00:37:36.980 --> 00:37:38.840 - We'll be back again next week.