The next significant advancement in AI is focused on understanding the physical world, encompassing intuitive physics, fluid dynamics, thermodynamics, and quantum mechanics. This shift promises transformative benefits across industries like automotive, renewable energy, and materials development. A key player in this transition is numerical simulation, which helps generate high-quality data for AI training. Drawing from experiences with DeepMind’s AlphaFold project, the author highlights the critical role of real experimental data, yet argues that relying solely on it may be limiting. Instead, incorporating established physics laws into neural networks could improve AI efficiency, although current methods like Physics-Informed Neural Networks (PINNs) have limitations.
Moreover, utilizing numerical simulators for creating datasets presents a viable alternative, offering insights in fields such as nuclear fusion, weather forecasting, and materials discovery. To democratize physics dataset generation, the author’s company, Inductiva, combines open-source simulators with cloud capabilities, facilitating easier access for researchers and engineers.
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