Towards a Physics Foundation Model
19 hours ago
- #Physics Foundation Model
- #Machine Learning
- #Transformer Models
- Foundation models have revolutionized NLP with a 'train once, deploy anywhere' approach.
- A Physics Foundation Model (PFM) could democratize high-fidelity simulations and accelerate scientific discovery.
- Current physics-aware ML models are limited to narrow domains and require retraining for new systems.
- The General Physics Transformer (GPhyT) is trained on 1.8 TB of diverse simulation data.
- GPhyT demonstrates foundation model capabilities for physics, simulating various phenomena without knowing underlying equations.
- Key breakthroughs include superior performance across domains, zero-shot generalization, and stable long-term predictions.
- This work opens the path toward a universal PFM, transforming computational science and engineering.