tensorflow-physics-ml
TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
Best use case
tensorflow-physics-ml is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
Teams using tensorflow-physics-ml should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tensorflow-physics-ml/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tensorflow-physics-ml Compares
| Feature / Agent | tensorflow-physics-ml | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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