tensorflow-physics-ml

# TensorFlow Physics ML ## Purpose Provides expert guidance on TensorFlow for physics applications, including physics-informed neural networks and neural network potentials. ## Capabilities - Physics-informed neural networks (PINNs) - Neural network potentials (NNP) - Normalizing flows for density estimation - Graph neural networks for molecular systems - Automatic differentiation for physics - TensorBoard experiment tracking ## Usage Guidelines 1. **Architecture Design**: Build appropriate neural network architectures 2. **PINNs**: Incorporate physical constraints in loss functions 3. **Potentials**: Train neural network interatomic potentials 4. **GNNs**: Use graph networks for molecular systems 5. **Training**: Monitor and optimize training with TensorBoard ## Tools/Libraries - TensorFlow - DeepMD-kit - SchNet