Scan to join WeChat groupTensorFlow 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
- Architecture Design: Build appropriate neural network architectures
- PINNs: Incorporate physical constraints in loss functions
- Potentials: Train neural network interatomic potentials
- GNNs: Use graph networks for molecular systems
- Training: Monitor and optimize training with TensorBoard
Tools/Libraries
- TensorFlow
- DeepMD-kit
- SchNet