TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
name tensorflow-physics-ml description TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models allowed-tools ["Bash","Read","Write","Edit","Glob","Grep"] metadata {"specialization":"physics","domain":"science","category":"data-analysis","phase":6} 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 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