xgxgnpu

xgxgnpu / Physics-informed-vibe-coding

Public

The first open-source repository dedicated to PINN research via Vibe Coding. Complete JAX-GPU implementations of PINN algorithms with Chinese tutorials. 首个采用 Vibe Coding 进行 PINN 研究的开源仓库。

deep-learning jax neural-tangent-kernel pde-solver physics-informed-neural-networks
18
0
100% credibility
Found Mar 17, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

A set of complete, runnable examples using AI to solve real physics equations from research papers, like heat flow, Poisson problems, and wave equations.

How It Works

1
🔍 Discover the project

You stumble upon this exciting collection of AI-powered physics experiments on a code-sharing site.

2
📥 Grab the files

Download the simple folder to your computer so you can try it out yourself.

3
📂 Pick a physics puzzle

Choose one ready example, like heat spreading or waves bouncing, that catches your eye.

4
🚀 Start the magic

Run the easy one-click script and watch the AI solve the physics problem step by step.

5
📊 See the results appear

Beautiful pictures and numbers pop up, showing exact solutions and how well it worked.

🎉 Master a physics challenge

You've just reproduced cutting-edge science results, feeling like a researcher without writing any code!

Sign up to see the full architecture

4 more

Sign Up Free

Star Growth

See how this repo grew from 18 to 18 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is Physics-informed-vibe-coding?

This repo delivers complete JAX-GPU scripts for training advanced physics-informed neural networks on 1D PDE benchmarks like heat, Poisson, and wave equations. Run a single Python file per case to compare plain MLPs against Fourier feature and multi-scale variants, with optional NTK adaptive weighting for balanced losses. It spits out L2 errors, NTK spectra, publication-ready plots, and saved data—reproducing results from top papers like Wang & Perdikaris without writing code, all via a "Vibe Coding" workflow where AI handles implementation under human guidance. Chinese tutorials make it accessible for non-English speakers.

Why is it gaining traction?

As the first open-source project blending Vibe Coding with PINN research, it stands out for zero-setup reproducibility on GPU: just `python script.py` and get metrics, figures, and checkpoints instantly. Developers skip weeks of debugging JAX autodiff and samplers, jumping straight to architecture comparisons and NTK diagnostics that reveal why standard PINNs fail on multi-scale problems.

Who should use this?

Scientific ML engineers benchmarking PINNs for fluid dynamics or heat transfer sims. Grad students replicating JCP/CMAME papers for theses. JAX users exploring NTK theory on real PDEs before custom projects.

Verdict

Grab it for quick PINN baselines—18 stars and 1.0% credibility score signal it's an early first GitHub repo in its niche, with solid docs but no tests yet. Run the cases yourself to validate before depending on it.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.