Universal Physics Simulation: A Foundational Diffusion Approach

• URL: http://arxiv.org/abs/2507.09733v1
• Date: Sun, 13 Jul 2025 18:12:34 GMT
• Title: Universal Physics Simulation: A Foundational Diffusion Approach
• Authors: Bradley Camburn,
• Abstract summary: We present the first foundational AI model for universal physics simulation that learns physical laws directly from boundary-condition data.<n>Our sketch-guided diffusion transformer approach reimagines computational physics by treating simulation as a conditional generation problem.<n>Unlike sequential time-stepping methods that accumulate errors over iterations, our approach bypasses temporal integration entirely.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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