Tensorflow physics ml
Skill a5c-ai/babysitter/library/specializations/domains/science/physics/skills/tensorflow-physics-ml
TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate modelsFrom its SKILL.md
npx -y skills add a5c-ai/babysitter --skill tensorflow-physics-mlAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
1.4 KB, 166 tokens by cl100k_base, as published. Nobody here has run it
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
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.