physics-informed-neural-networks
PINNs, scientific machine learning, and embedding physics into neural networks
Source facts
- Repository
- NeuralBlitz/Mito
- Last source activity
- March 22, 2026 at 13:29
- Detected SKILL.md language
- English
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SKILL.md
Source instructions · Read-only preview- name
- physics-informed-neural-networks
- description
- PINNs, scientific machine learning, and embedding physics into neural networks
- license
- MIT
- compatibility
- opencode
- metadata
- {"audience":"researchers","category":"machine-learning"}
## 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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