# Physics Informed Neural Networks

> PINNs, scientific machine learning, and embedding physics into neural networks

- Skill: `neuralblitz/physics-informed-neural-networks` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuralblitz/physics-informed-neural-networks`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuralblitz/physics-informed-neural-networks/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: NeuralBlitz (https://skillmd.com/u/neuralblitz)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/neuralblitz/physics-informed-neural-networks

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## 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