Differentiable Physics: A Position Piece
It proposes a new paradigm for modeling physical phenomena, potentially impacting researchers in computational physics and machine learning.
The paper surveys differentiable physics, which integrates differentiable programming with classical numerical methods to model physical systems, enabling tasks like parameter estimation and solving differential equations.
Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation. We survey the rapidly growing literature of differentiable physics techniques and highlight methods for parameter estimation, learning representations, solving differential equations, and developing what we call scientific foundation models using data and inductive priors. We argue that differentiable physics offers a new paradigm for modeling physical phenomena by combining classical analytic solutions with numerical methodology using the bridge of differentiable programming.