Nangs
Nangs is a tool that uses neural networks to solve partial differential equations (PDEs), allowing for efficient and accurate simulations of complex physical systems.
- •When simulating fluid dynamics or heat transfer in complex geometries
- •When solving PDEs with nonlinear or high-dimensional terms
- •When requiring fast and scalable solutions for large-scale PDE problems
nangs.solve_pde(problem='burgers', num_steps=100, num_cells=1000, learning_rate=0.001)
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/nangs && curl -fsSL https://workflowstacks.com/api/skills/nangs/claude-skill -o ~/.claude/skills/nangs/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Nangs is a tiny Jupyter Notebook project (~400 lines across 17 code files). Setup is light: install Python, run one command. Last commit 45 months ago, Apache-2.0 license, has a test suite.
- 1README.mdStart here — what it does and how to install it
- 2nangs/__init__.pyInside nangs/ — the main logic begins here
- 3examples/01_adv1d.ipynbA worked example — copy this to get going
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/nangs && curl -fsSL https://workflowstacks.com/api/skills/nangs/claude-skill -o ~/.claude/skills/nangs/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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