Physics-Informed Neural Networks
Exploring how physics-based constraints can be embedded into neural network architectures to improve generalization and data efficiency.
Overview
Traditional neural networks require large amounts of labeled data. By incorporating known physical laws as inductive biases, we can train models that respect conservation laws, symmetries, and other domain-specific constraints.
Key Ideas
- Embedding PDEs as soft constraints in the loss function
- Architecture design that respects known symmetries
- Reduced data requirements through physics-informed regularization