{"ID":2895279,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09733","arxiv_id":"2507.09733","title":"Universal Physics Simulation: A Foundational Diffusion Approach","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 \u003e 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.","short_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 go...","url_abs":"https://arxiv.org/abs/2507.09733","url_pdf":"https://arxiv.org/pdf/2507.09733v1","authors":"[\"Bradley Camburn\"]","published":"2025-07-13T18:12:34Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}