- Hello, and welcome to the Physics World weekly
- podcast. I'm Hamish Johnston.
- My guest is David Wheater,
- who is vice president
- automotive
- at UK and US based PhysicsX.
- An aeronautical
- engineer by training, Dave spent much of his
- career using aerodynamics
- to improve the performance
- of formula one racing cars for several different
- f one constructors.
- Now his focus is on using
- AI based large physics models
- to design better passenger cars and other vehicles.
- This involves combining
- computational
- fluid dynamics
- simulations
- with wind tunnel experiments and real world testing.
- As Dave explains in this wide ranging conversation,
- which also looks at career opportunities
- for physicists
- in this multidisciplinary
- field.
- Hi, Dave. Welcome to the podcast.
- Hi, Hamish. Thank you very much for having
- me.
- So Dave, can we start out? Can you
- tell us a bit about yourself?
- Who who are you? What's your background? And,
- what's your experience in the automotive industry?
- Sure.
- So I am technical director for automotive at
- PhysX.
- That means I I lead automotive activities and
- also competition engineering,
- based on my background.
- So what does that involve?
- Working with customers,
- on the ground.
- You know, we we co engineer
- our our solutions with with customers in order
- to kind of quickly,
- build value. We have
- experienced engineering teams, cross functional teams across
- engineering, machine learning, data science, and software. And,
- you know, living
- side by side with our customers on-site,
- you know, we we we really learn about
- the real world of our customers and,
- and and what we need to do in
- the hands of engineers.
- So I I work there, and I also
- coordinate across the other key pillars of our
- company
- for automotive.
- We have a research group. We'll talk later
- about their work on the large physics model.
- But, yeah, that's a really world class group,
- contributing to academia. For example, a spotlight paper
- at NeurIPS,
- one of the leading machine learning conferences.
- Also, you know, keeping us at the cutting
- edge of research for the benefit of our
- customers.
- Recent example there,
- well, very recent, in fact, Transolver three, which
- is a a kind of machine learning architecture
- similar to a,
- sort of transformer
- used in large languages but architected for physics.
- That was,
- published in academia late February, and we're already
- shipping that onto our platform.
- And then finally, you know, we've got an
- amazing,
- AI and and and product
- group who who build a platform that that
- everything is based on and that allows us
- to deploy our end to end AI workflows
- into our customers'
- most most difficult physics problems.
- And and and there, you know, we need
- to make sure that we're integrating with the
- way our customers already work. So a lot
- of work around,
- kind of hook hooks into existing simulation and
- CAD tools
- and the right level accessibility for engineers.
- So, you know, my my work,
- the customers and understanding the customer problems is
- is really useful for for working with those
- groups.
- I should say I've I've been at PhysicsX
- for nine months,
- and and and it's been, you know, an
- amazing experience,
- working with that kind of diverse group.
- And and prior to PhysicsX, I I I
- spent quite a few years, twenty five years
- in Formula One,
- starting my life as an aerodynamicist
- at at Benetton.
- And,
- by the mid two thousands, I was leading
- the aerodynamics
- development
- across half the car for Renault f one.
- You know, that was still in the days
- of fairly small aerodynamics departments, but
- we were an innovative
- group and and launched
- 2005
- with with some pretty novel ideas.
- One of which, coincidentally, was developed by our
- cofounder,
- Robin Talui, which was a a mass damper.
- And I worked on a
- a a novel rear suspension system, which we
- introduced that year to replicate the error effect
- of of some of the floor that had
- been removed due to regulation changes.
- And and that really kind of cemented,
- the championship
- win in 2005
- and 2006,
- and that was at a time when you
- could win championships with with novel ideas.
- If I jump forward to my later life
- in Formula one,
- 2018, I became aerodynamics director at Williams and
- then,
- aerodynamics technical director at Alpine.
- Much larger departments by then, real machines
- of of development
- where winning takes just relentless pursuit of performance
- over time.
- And and that's where I I met PhysicsX,
- and and they stepped into the picture with
- me as a customer.
- We did some exciting work together. I can't
- talk about the details, but, yeah, the the
- impact
- was what caught my attention,
- not just that we we could do things
- much faster,
- but but the real transformation came from what
- we could do differently with that speed.
- In our case, reacting to results,
- over a race weekend in a in a
- way that just wasn't possible before.
- And and, David, I I think it's easy
- for a physicist to imagine how aerodynamics
- can be very important in Formula one. But
- what about the design of of of modern
- cars, you know, the sort of car that
- I would drive to the supermarket?
- Can significant improvements in fuel efficiency, handling, and
- safety
- still be gained by improving aerodynamics?
- Yeah. Absolutely. And and and as you say,
- Hamish, you know, in in in Formula one,
- aerodynamics and downforce is is is the currency.
- You know, 11%
- of downforce is a tenth of a second,
- and and that can be a a championship
- changer for for a team in a tight
- championship.
- And and I should say in, you know,
- in,
- Formula One, it's not just about the downforce.
- It's also about the drivability. You know, drivers
- but they don't drive to the peak. They
- drive to the trough if you've got variations
- in downforce over over car conditions.
- And so
- in automotive,
- kind of passenger vehicles,
- aerodynamics plays just as critical a role in
- in in slightly
- different ways,
- and gets into just as many systems on
- the car. The obvious one being, of course,
- the external shape, the focus there more on
- drag than than downforce from Formula one.
- You know, above 30 miles an hour,
- most of most of your energy consumption is
- is going into overcoming air resistance. So
- good fuel efficiency
- absolutely needs great aerodynamics, whether that's kind of
- the obvious details that you see on the
- car, the spoilers, etcetera,
- or or the lot less obvious like the
- tire shapes, which can can be real really
- important.
- But also in the thermal management,
- systems like powertrains,
- NVH, which is the noise vibration harshness, and
- that's about controlling the turbulence of the flow
- around the car,
- and, of course, passenger comfort, HVAC, which is
- heating, ventilation, and aircon.
- Those are really important attributes for for, you
- know, OEMs developing their cars,
- and and, you know, pitching them,
- at the top of the market.
- And so today, how how are vehicle
- aerodynamics
- simulated?
- Is it, is it sort of a I'm
- guessing you're you're using some pretty powerful,
- computer models,
- to simulate how, you know, for example, how
- air flows around a car, how it interacts
- with the tires, that sort of thing?
- Absolutely.
- So,
- you know,
- traditionally,
- over the last few decades,
- we've been using these numerical simulations
- for for fluid dynamics or computational fluid dynamics.
- And and these, you know, these simulations
- take
- the the the flow that governs their dynamics,
- the Navios Stokes equations generally,
- and and applies them to
- a discretized
- car and and environment so that a computer
- can solve that numerically.
- And and as you suggest, you know, that
- that that takes a lot of compute horsepower.
- You know, the the the simulations can run,
- if if if you're talking about, unsteady simulations,
- they they they can run for days.
- And and, you know, to get even to
- get to those simulations, you obviously need to
- draw a car and that's likely to be
- in CAD and take some time. But these
- numerical simulations are are pretty fickle. You know,
- to get those numerics to work,
- you need a perfectly closed volume to to
- to solve the calculations, and that actually often
- takes multiple days of work to go from
- something that looks, you know, like a kind
- of finished car on the screen to something
- that you can get through a simulation. And
- and and there's a hidden effort there, which
- aerodynamic
- aerodynamicists
- like myself know only too well.
- So so when we start talking about these
- AI tools, you know, an important part of
- speed up is is also in the the
- the kind of the preparation
- effort that goes into to
- a a current simulation.
- There there's also the post processing side. So,
- you know, once you run that that simulation,
- you'll you'll want to extract the the things
- you really care about. It might be the
- lift. It might be the downforce.
- So so so that you generally set up
- some kind of process around that.
- And and also maybe to understand the flow
- field, you you might take a few slices,
- through through the airflow,
- which you'd sort of save down,
- because that that simulation file is normally
- pretty huge,
- and impractical to save.
- And you said these are big calculations.
- Are can can you do them in house?
- Or do you have to rent time on
- a supercomputer?
- Or do you do a bit of both?
- Yeah. I mean,
- most,
- kind of most automotive players will will will
- have their HPC resource.
- Oft often that is in house, and, you
- know, these are these are big big investments,
- but but increasingly,
- kind of cloud provision,
- and and as so it's moving towards GPUs,
- on on GPU clusters,
- are pretty typical.
- And what about,
- you know, so I I suppose proving
- the the the worth of these simulations.
- How do you do do you have to
- compare
- experimental data and,
- simulations
- in order to to know that you're on
- the right track? I mean, do do you
- have to actually build a model
- of the car and and stick it in
- a wind tunnel? I'm guessing that's probably what
- you're trying to avoid because that would be
- very expensive. So how how do you confirm
- that your that your simulations
- are working?
- Yeah.
- Absolutely,
- experiments
- are an important part of the picture. And,
- if I kind of think about my formula
- one days,
- then, you know, we we we were using
- the wind tunnel
- very heavily to kind of correlate and tune
- our our CFD. In the end, you know,
- the CFD is a is a model of
- a a model of your reality.
- And and so, you know, having that experimental
- data,
- albeit it's, you know, it's it's time consuming
- and it's and it's expensive
- is is an important part of validation
- for sure.
- I see. But,
- and and and so now I mean I
- mean, maybe this is a good time to
- to start talking about
- AI.
- So, David, before the advent of AI, how
- were experimental
- and simulated
- aerodynamics
- data used to design cars?
- Great question. Thanks, Hamish.
- So during my f one experience, although I
- think the answer is similar for for sort
- of passenger cars as well.
- You know, when I started at Benetton in
- 2000, it was really all about wind tunnel
- testing,
- and just doing that as much as possible
- and as fast as possible.
- But it was expensive. And, you know, even
- with, you know, f one speed, it was
- relatively slow weeks between loops
- and limited in terms of the feedback that
- you could get,
- from those tests. You you'd get force from
- the balance and maybe a few pressure sensors.
- Then on the CFD side, we had, at
- that stage, just one engineer, and their entire
- focus actually was on the the airbox, which
- is is the sort of feed behind the
- driver's head going to the engine.
- Otherwise, it was all wind tunnel. And there
- was a lot of skepticism at the time
- about that simulation with engineers
- saying they didn't believe the CFD and and
- not using it.
- And then through the two thousands,
- you know, we we saw that the the
- teams that really thrived were the ones that
- saw CFD as something complimentary.
- You know, it was much, much faster than
- than wind tunnel testing. It provided flow field
- insight that that you couldn't get from the
- wind tunnel.
- So with results in a day rather than
- weeks, that massively, you know, accelerated how you
- could develop the car, and that insight that
- you got really helped inform next steps. So
- then if if we roll through to sort
- of 2020,
- then
- your CFD was absolutely a given.
- And and and, actually,
- the the majority of team principals in f
- one voted in an f one meeting
- to turn off wind tunnels completely for for
- cost saving. But, actually, I think that that
- kinda misses the point, to one of your
- previous questions.
- You know, the these these tools are are
- best when you use them together. And I
- think we're living through the same moment with
- with AI. You know, there's absolutely,
- some skepticism,
- and and and some some engineers see this
- as a swap in for CFD. But I
- think the most successful users I see are
- are those that are are using AI as
- another layer,
- giving that massive acceleration that it offers, but
- working alongside the the tools and expertise already
- in place.
- I see. And and may I suppose moving
- on to AI, PhysicsX develops
- large physics models for aerodynamics
- and other applications.
- What is a a large physics model? And
- how does it differ from
- conventional
- computational approaches to aerodynamics?
- Yeah. So so if I I kind of
- start with the differences.
- So
- machine learning physics model's different in a in
- a couple of different ways.
- Unlike traditional computational
- approaches,
- machine learning model,
- it it doesn't need to be told explicitly
- what the laws of physics
- are.
- It learns it learns that from the data
- it sees and and, you know, we we
- use architectures that are suited to specific physics
- problems.
- And and and in doing that, it becomes
- much more flexible than a traditional CFD tool.
- It it doesn't require the same geometry preparation
- we talked about earlier,
- and and it can learn not just from
- simulation data, but also from experimental data. So
- it can actually bridge the gap.
- It it can bridge the
- gap, from from simulation world to kind of
- ground truth.
- It it it's got some obvious benefits in
- terms of speed. Obviously, that that that's kind
- of well known, and and and those speeds
- can be up to, you know, 10,000 or
- more times. So so really game changing.
- And and that allows you to, as I
- mentioned
- in the example before, that really transforms what
- you can do,
- particularly when you get into solving multi physics
- systems. So,
- in in a traditional,
- maybe in an OEM, you you you might
- be solving problems
- that that span across aerodynamics.
- You've made it be in an engine design
- where you've got aerodynamics involved, but you've also
- got structures involved.
- And and solving those different physics goes across
- many groups and many groups of experts.
- But with with these models,
- then then you can solve those physics all
- at the same time. And because the models
- are more accessible than,
- than than sort of simulation
- tools,
- then you can put them direct in the
- hands of a designer.
- And and that really starts to, again, change
- the way you're working rather than just doing
- things a bit faster. You you really start
- to rethink how you're working.
- And and then finally, another aspect of these
- models is,
- that that they've they've got this concept of
- uncertainty
- quantification.
- So they they know how confident they are,
- and
- and and that's not just important from an
- engineer's point of view because we need to
- be building trust
- in in,
- in in the kind of use of of
- the results.
- But also from from the model point of
- view, we can use that uncertainty to drive
- the the simulations that the model needs to
- be accurate in the places that it needs
- to be. So we can have these, what
- we call, active learning loops,
- that that automatically trigger new set new simulations
- when when a model becomes unsure.
- And David, you,
- and your colleagues at PhysicsX
- have released a study called scaling physics AI
- for automotive
- aerodynamics.
- And it argues that publicly available datasets
- of automotive
- simulations
- are not sufficient to train
- large physics models.
- Why is this? And what are the consequences
- for those using
- AI to design cars?
- Yeah. So so I should maybe give one
- last piece of detail about the the these
- types of models and and just to sort
- of transition to the automotive large physics model
- that we've,
- developed at PhysicsX because I think that gives
- some context.
- And, you know, this is a pretrained model,
- so it's already seen a huge amount of
- data. And and that means it's something that
- we can take to a customer that's that's
- sort of ready to go out of the
- box and and can predict aerodynamics
- of previously unseen vehicles.
- And and and a a really powerful aspect
- of that is when we start fine tuning
- that model. So we take the model that
- we've developed in the house. We we fine
- tune it for a given customer's needs or,
- specifics of their car type and simulation approach.
- But underpinning all of that is is is
- the data, the high quality data that we
- need to build that model. And and that
- really comes to your question
- about
- the the need for,
- the the the need for a dataset,
- and
- our,
- our own our own dataset. So there's a
- there's a couple of well publicized,
- publicly available datasets, driver net and luminary shift
- SUV.
- And those were,
- both derived from sort of parametric
- morphing
- of of a small number of base configurations.
- And that that morphing is is a kind
- of where you take a geometric mesh and
- you kind of push and pull,
- to to give geometric variation.
- And, you know, these,
- the these these variations,
- obviously give give some diversity, but what we,
- you know, what we saw when we we
- started digging into these models
- was that they performed well on on when
- we tested them on geometries, which were very
- similar to those,
- datasets.
- But but really, they started to fail quite
- badly when we looked at realistic
- customer geometries, which which were of a different
- design.
- So to to address this shortcoming that that
- you've identified in your study,
- PhysicsX has created the p x NETCAR
- proprietary
- dataset.
- How did you develop this, and how does
- it compare to public datasets?
- Yeah. So,
- we we kind of having having drawn that
- conclusion that that, you know, we we needed
- this
- more diversity, so we built,
- what we call,
- the the Physics x Data Factory, and that's
- really an automated pipeline
- that can run many, many,
- simulations.
- In this case, STAR CCM plus runs,
- Reynolds' average average Navios Stokes simulations.
- So, you know, we we got a hold
- of 450
- quite distinct car shapes,
- from large scale three d model libraries.
- And then we
- produced our own kinda algorithmic
- morphing approach so that we could create
- around a 100 variants for for each of
- these baselines using a a tool called Blender,
- which is an open source three d modeling
- tool.
- And and we used a Latin hypertube
- sampling approach so that we knew we were
- kind of creating a a statistically,
- good coverage of of these geometries.
- And then we we rigorously validated,
- those results in terms of the kind of
- convergence and the correctness of the physics knowing
- that, you know,
- the the the quality of that data was
- critical to the performance of the model at
- the end.
- So that that allowed us to build PXnetcar,
- which now comprises
- over 30,000 simulations
- across those 450
- distinct,
- geometries. And, you know,
- comparing back to the driver net and luminary
- shift, which is where we started with, you
- know, between
- those two, that they they gave us 10,
- kind of 10 distinct geometry start points. So
- it's it's really a complete game changer
- scaling up that much.
- And and, you know, we wanted to be
- quite quantitative
- about the, the diversity we were generating. So
- we use something called pairwise chamfer distance,
- which basically measures between different geometries
- the the the kind of distance between all
- the points, and and it gives a a
- way of quantifying
- that diversity. And we could see as we
- build up that dataset that we were,
- that we were really building a a very
- diverse,
- set of classes, not just sort of minor
- morphs.
- And then when we started applying that,
- a large physics model in in practice, then
- that very clearly translated into much greater generalization
- performance. So, you you know, when we started
- showing it new geometries, new cars,
- that then the performance was much much higher
- than than than the models that we'd,
- trained on those narrower, luminary,
- and, and and driver net datasets.
- And and we've been talking about cars, David,
- but PhysicsX has a range of interests outside
- of vehicle design. Can you talk about some
- of the other sectors that you're developing
- AI tools for?
- Yeah. Absolutely. Yeah.
- I mean, we work on some incredibly exciting
- projects.
- And, you know, many of these we can't
- talk about because they're they're customer projects.
- I happen to be working on one which
- is, has been publicly announced, so that that's
- quite helpful.
- But starting more generally, you know, our our
- models add most value in in in sectors
- where
- complex physics simulation
- is right at the heart of engineering decision
- making or operation. And, generally, you know, the
- time that we spoke about earlier to solve
- those simulations
- can be a real bottleneck for for engineers
- or product development.
- Commonly, we see this across aerospace and semiconductors,
- energy, and materials. So, you know, this is
- where we we're doing a lot of work.
- And and, also, it's not just about design,
- but because these models can solve more or
- less real time, they become ideal for manufacturing
- and and operation processes.
- You
- know, just to give a small sense of
- the complexity, a recent project
- we developed was a a plasma reactor for
- for converting industrial waste gases into synthetic fuels.
- So basically taking, gases including c o two
- and then converting that into carbon monoxide,
- which which then went on into these,
- synthetic hydrocarbons.
- And, you know, we had to model all
- the chemical reactions, the mixing of flow through
- the reactor,
- and and the thermal modeling, of course, to
- to make sure that kind of critical components
- didn't overheat.
- And with with this technology, we can do
- all of that simultaneously.
- So it's
- massive, massive speed up.
- And, you know, we we we reduced the
- r and d process by a couple of
- years, and and in the end, had a
- machine that was giving a tenfold increase in
- react productivity, which is, you know, an absolute
- game changer.
- Moving moving on to the project that I'm
- involved in,
- so so this is working with GB one,
- which is The UK's entrant to the twenty
- seven America's Cup.
- And it's I think it's an interesting example
- because, again, it's it's a very advanced engineering
- workflow. And and and for some context for
- those maybe not aware,
- America's cup boats are 75 foot hydrofoiling
- sailing yachts,
- often better described as flying machines.
- You know, they they they go pretty crazy
- speeds in excess of four and a half
- times wind speed, which as an engineer takes
- a bit of wrapping your head around,
- which means they go they go above a
- 100 kilometers an hour.
- And I was lucky to join the team,
- for some testing in Sardinia a couple of
- weeks ago. And and then you really do
- get a sense of of the speed when
- this boat is is moving towards you at
- that that kind of speed.
- And and, you know, like in f one,
- it's very much a competition between engine engineering
- departments.
- So they've got very, very refined development processes.
- But one of the big remaining challenges is
- just a massive dimensionality,
- of of the design and operating space that
- that the engineers have to contend with. And
- this is where boats are different from f
- one cars.
- You know, f one cars, you've got the
- steering and and accelerator brake pedal as input.
- But with the boats, you've got multiple controls
- across the foils,
- the sails,
- you know, the the sail shape itself, you've
- got many controls, plus you've got the the
- wind effect.
- So not only are there all these design
- parameters, but there's also all these operational ones
- as well. And and that's kind of where
- we've found,
- you know, a a a a really good
- fit between GP one and PhysicsX because
- our,
- kind of
- physics aware architecture
- can learn all those complex interactions, aerodynamics,
- hydrodynamics,
- and and predict near real time
- both the flow around the car, but also
- the,
- the the the loads the loads on the
- sorry. Around the boat, I should say.
- And also the the the the loads that
- that then feed into GB one simulation so
- we can get a a a kind of
- performance output.
- And and and also in, you know, in
- the structural domain as well. So
- we we can give a really kinda complete
- optimization.
- So, you know, that that's,
- using our platform, taking the learning from the
- research group, the, you know, the the the
- LPM
- development that we spoke about, we can directly
- translate very quickly into something that's been useful
- for the GB one team.
- And,
- you know, in in in doing that, we
- we're building very much on their expertise and
- their physical insight.
- But with with such a vast,
- you know, vast space to explore,
- the these tools are a complete game changer.
- And,
- you know,
- one one of one of the points we
- touched on earlier was, you know, bound the
- the confidence of these models. And when we
- want to start exploring such,
- fast spaces,
- we can, again, we can use that uncertainty
- to kind of drive where we collect data.
- So we we we're starting relatively small with
- the dataset,
- and and then as we start optimizing and
- the optimizer is exploring the design space, we
- can collect more and more data.
- So,
- you know, what we're seeing is is all
- of the all of the,
- lessons that we've learned in the,
- large physics model are are giving us this
- leg up with with our GPU one work,
- and that's that's really exciting.
- When when you're doing the,
- the the work, you know, with the with
- the boat,
- are you are you just looking at
- design,
- or are you also
- looking at
- the the way in which the boat can
- be sailed?
- You know, looking for,
- I don't know, maybe different ways of doing
- things or the best way of of sailing
- the boat in addition
- to,
- I suppose, adjust,
- for lack of a better term, the the
- fluid dynamics
- of the boat? Or is that is is
- that something different or maybe something that you
- could look at in the future?
- Well, yeah. Great question. And and maybe there's
- a bit of both there because,
- you know, even at the design stage, if
- we wanna optimize the design, we need to
- consider how the boat's gonna be sailed.
- You know, we we can't do it in
- isolation if we want to really kind of
- get to a optimum performance. So, you know,
- we are considering,
- many aspects of how the boat will sail,
- you know, as we're as we're going through
- that design iteration.
- But to your, you know, to your point,
- we we can also use these types of
- models in operations. So we're we're we're absolutely
- thinking about, you know, how we might be
- able to, kind of take the next step
- and,
- deploy these models, whether that's sort of,
- give it giving kind of an almost advice
- to the sailors on on on optimum,
- sailing strategies or or or in in in
- control.
- And finally, here at Physics World, we're always
- really interested in exploring
- careers
- for physicists.
- And, I'm sure that some of our listeners
- would be interested in a career in the
- commercial development of large physics models.
- What sort of employment
- prospects are there in the sector?
- And could could you maybe give a a
- bit of career advice for someone who who'd
- like to to get a job,
- developing these models?
- Yeah. Absolutely. And, you know, my own journey
- to physics x is is is maybe not
- a typical one.
- But but maybe maybe there's a little bit,
- you know, in in that journey that I
- I still sort of take away.
- You know,
- when when I started out in f one,
- it was definitely not a well trodden path.
- And, you know, I was writing a lot
- of letters, and I was looking for any
- break I could get.
- And and and, actually, my break came from
- a rejection from what was then British American
- Racing. You know, they wanted to employ some
- of the experience, but they,
- they they were on a notice period. So
- there was, there there was a a a
- few months where there was a vacant seat.
- And so I
- said, okay. Can I can I just come
- and, you know, sit there and and learn
- for a bit? And and and, actually, I
- had the chance while I was there to
- go and do some, testing in Italy.
- We we were,
- basically kind of putting wires across the the
- race car to understand deflection effects. And then
- I learned a huge amount. It was a
- a great education. And, you know, it was
- a a lesson definitely for me in, you
- know, looking for those opportunities even when they
- come in in unexpected ways.
- And, you know, that
- gave me my career in f one and
- and then the the opportunity,
- and the connection with PhysicsX also came with
- some serendipity.
- You know, it wasn't an opportunity I anticipated,
- but, you know, after my experience as a
- customer, not just the the impact
- that I saw in f one, but the
- scalability of this of a wider industry.
- You know, I I knew it would be
- an amazing place to be. And so, again,
- kind of looking for that opportunity
- to to kind of make the connections.
- And now, you know, now I am at
- PhysicsX, and I'm I'm seeing, you know, the
- work that we're doing on on large physics
- models.
- You know, it's incredibly multidisciplinary.
- Obviously,
- it needs domain expertise.
- Also, you know, we we need simulation engineering
- to generate and validate our our datasets. We
- talked about how important they are. And the
- machine learning research spanning areas like geometric learning,
- physics informed methods,
- and and a lot of statistics and uncertainty
- quantification. So all, you know, really important ingredients.
- And,
- then familiarity
- with
- pretrained models is increasingly relevant, and that could
- come from language or image or video. So,
- you know, many of these
- same ideas and architectures,
- kind of transfer into this domain and and
- and and so those experiences
- are all relevant.
- I I guess what I've seen is,
- it's not just about
- one one skill that sort of makes the
- difference for us. It is the collaboration across
- physicists and simulation engineers and machine learning researchers,
- infrastructure engineers,
- you know. And and
- from my own experience, often, you
- know, there there's a different language there eve
- even for the for the same problem that
- you're trying to solve. And so bridging the
- gaps between all these different experts is where
- a lot of the value is created.
- And so, you know, I think, of course,
- being an expert in one area is is
- absolutely the foundation of your credibility, and there
- are, you know, the areas that I've talked
- about there. But, also, I would advise you
- know,
- try and develop that familiarity
- and understanding across the full scope
- because the people that can kind of really
- thrive are those that can have a meaningful
- conversation
- both with a a maybe a CFD engineering
- engineer in the morning or or a system
- architect in the afternoon and and and and
- join those dots up.
- And
- one of one of the things I've loved
- about being at PhysicsX is we we hot
- desk here. And, you know, every day I
- find myself sitting someone,
- sitting next to someone with a really deep
- expertise
- in in something fascinating that I've learned from.
- So I suppose my overall advice there is,
- you know, be curious and be open minded.
- Yeah. You never know where where the next
- opportunity will come from or the next idea.
- And and and there's a good chance the
- next best idea for you will come from
- someone else. So, yeah, stay receptive to people
- around you and,
- you know, always ask for feedback,
- because it's it's really the best gift that
- you can get.
- So I think that would be that would
- be my advice.
- Well, it sounds I mean, it sounds like
- a fascinating field
- that you're in.
- And, you know, especially for,
- you know, for for for a physicist or
- a mathematician
- or, I suppose, even with you, a a
- chemist,
- that, you you know, there that there are
- opportunities.
- You know, I suppose AI gets a a
- lot of bad press these days, but,
- it sounds like it's really opening up,
- a a a whole new world when it
- comes to,
- to design.
- Yeah. You you absolutely. And and and, you
- know, I think,
- the the AI in the physical space is,
- you know, is is quite a different beast,
- and it's it's really accelerating.
- And as you mentioned, you know, we we
- we
- have experts in in chemistry as well as
- physics,
- and,
- you know, bringing bringing bringing those experts together
- as well as, of course, the the kind
- of fundamental
- AI behind the under the hood, you know,
- that's kind of where the magic happens.
- Well, that's great. Thanks, David. Thanks so much
- for, for joining us today and,
- talking about your career and your work at,
- PhysicsX. It's been a pleasure. Thanks very much,
- Hamish.
- I'm afraid that's all the time we have
- for this week's podcast.
- Thanks to Dave Wheater of Physics x for
- an enlightening discussion
- of how large physics models
- are making cars
- more efficient.
- I'm Hamish Johnston, and our producer is Fred
- Isles.
- The music for this episode is called one
- three seven, and it was composed and performed
- by the physicist
- Philip Moriarty.
- We'll be back again next week.