Universal Physics Simulation: A Foundational Diffusion Approach

TL;DR

This paper introduces a universal physics simulation model using a diffusion transformer that learns physical laws directly from boundary data, enabling accurate, generalizable, and boundary-guided steady-state solutions without explicit equations.

Contribution

It presents the first foundational AI model for universal physics simulation that learns from boundary conditions, bypassing traditional equation-based methods.

Findings

Achieves SSIM > 0.8 for steady-state solutions

Generalizes across diverse physics domains

Enables physics discovery through learned representations

Abstract

We present the first foundational AI model for universal physics simulation that learns physical laws directly from boundary-condition data without requiring a priori equation encoding. Traditional physics-informed neural networks (PINNs) and finite-difference methods necessitate explicit mathematical formulation of governing equations, fundamentally limiting their generalizability and discovery potential. Our sketch-guided diffusion transformer approach reimagines computational physics by treating simulation as a conditional generation problem, where spatial boundary conditions guide the synthesis of physically accurate steady-state solutions. By leveraging enhanced diffusion transformer architectures with novel spatial relationship encoding, our model achieves direct boundary-to-equilibrium mapping and is generalizable to diverse physics domains. Unlike sequential time-stepping methods that accumulate errors over iterations, our approach bypasses temporal integration entirely, directly generating steady-state solutions with SSIM > 0.8 while maintaining sub-pixel boundary accuracy. Our data-informed approach enables physics discovery through learned representations analyzable via Layer-wise Relevance Propagation (LRP), revealing emergent physical relationships without predetermined mathematical constraints. This work represents a paradigm shift from AI-accelerated physics to AI-discovered physics, establishing the first truly universal physics simulation framework.