AI Pioneer Anima Anandkumar on Building Foundation Models for Physics
AI pioneer Anima Anandkumar highlighted the shift from language models to physics-based AI, focusing on how neural operators are being used to model complex physical phenomena like weather and fusion reactors. Traditional physics simulations using partial differential equations (PDEs) are computationally expensive, and applying neural operators can dramatically speed up scientific discovery and engineering design. This shift could revolutionize fields ranging from meteorology to clean energy. Unlike standard neural networks, neural operators learn mappings between infinite-dimensional function spaces, allowing them to solve PDEs without requiring a fixed discretization or retraining for new grids.
## BACKGROUND
Modeling the physical world often requires solving Partial Differential Equations (PDEs), which describe how physical quantities change over space and time. Traditional numerical solvers are highly accurate but slow, prompting researchers to develop Physics-Informed Machine Learning (PIML) and neural operators to create faster, data-driven surrogate models.