All engineering companies want to build high-quality products as efficiently and quickly as possible. Physics-based simulation or CAE can provide answers virtually and faster without building prototypes or performing tests. However, in a recent survey among 163 engineering leaders conducted by the market research institute Forrester*, 55% of leaders said that they found CAE too inaccurate. Despite years of continuous progress on CAE methods, there are still many problems for which solving PDEs does not provide accurate insights – either because the underlying physics is too complex, or the boundary conditions are insufficiently known. To give an example, calibrating a P2D model for battery simulation requires so much data of a real battery cell prototype, that the battery must already have been manufactured and have undergone a large number of tests before the model becomes useful. In a case like this, where the physics is intractable, data-driven simulation can be a faster and easier way to model and optimise system performs. Intractable physics problems are everywhere in engineering, from materials science to dynamic system behaviour. This talk will look at various industrial case studies in which data-driven modelling was used to accelerate product development, including the Porsche Jota race team who used data-driven models for tire degradation and won Le Mans, Honeywell who found that data-driven methods can beat CFD in ultra-sound flow measurement, Kautex Textron who developed a more accurate method to model fuel sloshing noise, as well as BMW group who managed to predict the result of crash tests without CAE. The talk will end by focusing on battery simulation. In the author’s opinion, this is the most impactful area of engineering where machine learning can help model intractable physics, as the electrochemical relationships of batteries are notoriously hard to model and require a huge of amount of time-consuming testing. *Commissioned by Monolith