Universal Physics Simulation: A Foundational Diffusion Approach

2025/07/13 by Bradley Camburn, Camburn, Bradley
Computer Science · #65M06 #68T07 #78M34 #Artificial Intelligence (cs.AI) #Computational Physics and Python Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.2.6 #I.4.8 #J.2 #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2507.09733

openalex publication_date 2025/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• ControlAR: Controllable Image Generation with Autoregressive Models
• Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN
• A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
• U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
• Constrained Synthesis with Projected Diffusion Models
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
• Scalable Diffusion Models with Transformers
• PhysDiff: Physics-Guided Human Motion Diffusion Model
• Conditional diffusion-based microstructure reconstruction
• Fast Sampling of Diffusion Models with Exponential Integrator
• GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
• High-Resolution Image Synthesis with Latent Diffusion Models
• High-Resolution Image Synthesis with Latent Diffusion Models
• Crystal Diffusion Variational Autoencoder for Periodic Material Generation
• Physics-informed machine learning
• Diffusion Models Beat GANs on Image Synthesis
• Learning Transferable Visual Models From Natural Language Supervision
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Denoising Diffusion Implicit Models
• Denoising Diffusion Probabilistic Models
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Attention Is All You Need
• Layer Normalization
• U-Net: Convolutional Networks for Biomedical Image Segmentation

Related

• Physics-based Deep Learning
• Text2PDE: Latent Diffusion Models for Accessible Physics Simulation
• Towards a Physics Foundation Model
• Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
• PILD: Physics-Informed Learning via Diffusion