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 neur…