physical-world-models

This paper identifies two distinct failures in learned physical simulators—instability in long rollouts and inability to adapt to changed laws—and proposes separate structural solutions: symplectic integration for stability and explicit factorization for counterfactual generalization.

This paper introduces Separable Neural Architecture (SNA), a function class that combines neural approximation with tensor decomposition to efficiently solve parametric PDEs. The method achieves dramatic speedups (up to 150,000×) over traditional grid-based methods in engineering applications like laser powder bed fusion and material property prediction.