Nathan Labenz: uncertainty

Transcript context

…Yeah. Great question. So there are many different domains, as you can imagine. I mean, think about the complexity of a car, right? Now, today in the industry, a GM or another OEM is simulating the entire car when developing it, which means that every single component or sub-assembly within the car is being simulated, is being evaluated, and is being integrated on, right? So that means that we are, as an engineer, we are evaluating many different physics on the car. Aerodynamics is 1, specifically critical for EVs, right, on the range. You want to improve the range on your next EV. Crash safety for pedestrians and passengers is another big one. Then we also have thermal management, when we want to cool the batteries, hold the engine, but also for ventilation systems inside the car, right? Electromagnetism, when we want to build an acceleration of electric motors, right, there is a big electromagnetism aspect to it. And then structural dynamics, generally speaking, for the car, your ability to the chassis or different Rd. profiles, and so on and so forth. And just this thing, the main categories, of course, as you can imagine, then this is being divided into component thermal assemblies. Ultimately, with a company like GM, you have thousands and thousands of engineers that domain experts on this physics, on those components, so that they can iterate and improve each of these specific assemblies. So those are the big domains, essentially. Are all those different domains that you laid out now powered by a domain specialist model that has learned, for example, the intuitive physics of heat dissipation through a ventilation system? And how, if we just take that one example, if the old version was like actually having a simulation down to the level of I don't know if it was all this detailed, but going all the way down to molecules of air blowing through a space and how they bounce off of each other and what ultimately happens. What level of abstraction or sort of intuitive physics are we now able to get? How do we say to this like specialized model, here's a new design for a ventilation system. You like tell it, predict what's going to happen. What kind of inputs and outputs look like to those models? Are they trained on simulation data also? That's another thing that I've noticed is a real pattern. Yeah. So to the last question, these models, these A models can be trained both on simulation data, but also on test data, external data, right? Because today, even today, there are some phenomenon that we're not able to simulate very accurately with traditional solvers. In that case, what we can do is that if we cannot simulate them, we can measure them in wind tunnel or in test. loops, right? And we can gather the data and then train the corresponding AMLs. So this is also a path of data. This is what we call hybrid training, essentially, where you combine numerical simulation that can be no more fidelity because we're not capturing the physics very well and measurements. Now to your question before, how far have we gone into that space? To be very clear, today we are not fully replacing numerical simulation. The same way that we never fully replaced prototypes, we're still doing prototypes today in the industry, right? But we're doing much less prototypes and much later in the process, right? It is going to be the same and it's the same for numerical simulation. We're going to fully replace numerical simulations, but we're going to make a much smarter usage of it for the most mature stages of development. And it's going to be exactly the same with AI. AI, earlier on in the development process, we enable you as an engineer to explore a much richer space, to explore the right candidates, and then narrow down the one that you actually want to simulate to validate before going to prototypes, right, what we're saying. Today, LGRM is such a specific, accurate field that there is not one foundational model that can be used to solve the aerodynamics. on every car, right? There is research definitely going in that direction and we are sort of the forefront of it at our concepts. However, it is not yet able to capture the level of affinity that you would need, that every car OEN would need to be able to deploy it on the shelf. What does that mean? That means that we are retraining and we are training the models, the company's specific data and numerical simulations.…

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