AGENT SKILL
physics-informed-neural-networks
PINNs, scientific machine learning, and embedding physics into neural networks
- License 许可
- MIT
- 所属仓库
- NeuralBlitz/Mito
- 最近更新
- 2026-04-21
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- physics-informed-neural-networks
- license
- MIT
- metadata
- { "audience": "researchers", "category": "machine-learning" }
- description
- PINNs, scientific machine learning, and embedding physics into neural networks
- compatibility
- opencode
03 SKILL.md 正文
What I do
- Build physics-informed neural networks
- Embed PDEs and physical laws into ML
- Solve inverse problems
- Combine simulation with data
When to use me
When combining physics with machine learning.
Key Concepts
- PINN architecture
- Physics loss terms
- PDE constraints
- Forward and inverse problems
- Domain knowledge integration
- Scientific ML
- Surrogate modeling
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