darelleascendant189/pytorch-pinn-coupled-spring-mass

View on GitHub by @darelleascendant189

🔍 Solve N-dimensional coupled spring-mass systems using a Physics-Informed Neural Network in PyTorch, without needing ground-truth data.

What's novel

🔍 Solve N-dimensional coupled spring-mass systems using a Physics-Informed Neural Network in PyTorch, without needing ground-truth data.

Code Analysis

9 files read · 3 rounds

Implements a standard Physics-Informed Neural Network (PINN) using PyTorch to solve N-mass coupled spring-mass ODEs via autograd-based residual loss, with evaluation against scipy's RK45 solver.

Strengths

Clean code organization with proper separation of model, loss, data, and utilities; correct physics formulation with well-documented theory in the notebook; sensible hyperparameter defaults with full CLI configurability.

Weaknesses

Critical bug where validation loss computation uses autograd inside torch.no_grad() context; zero test coverage; README is a generic template that misrepresents the project as a downloadable GUI application with broken links.

Score Breakdown

Innovation

4 (25%)

Craft

53 (35%)

Traction

8 (15%)

Scope

46 (25%)

Signal breakdown

Innovation

Not Fork+1

Code Novelty+0

Concept Novelty+1

Craft

Ci-1

Tests-1

Polish+0

Releases+0

Has License+5

Code Quality+16

Readme Quality+15

Recent Activity+7

Structure Quality+5

Commit Consistency+2

Has Dependency Mgmt+5

Traction

Forks+0

Stars+6

Hn Points+0

Watchers+0

Early Traction+0

Devto Reactions+0

Community Contribs+2

Scope

Commits+7

Languages+5

Subsystems+5

Bloat Penalty+0

Completeness+7

Contributors+6

Authored Files+8

Readme Code Match+3

Architecture Depth+3

Implementation Depth+8

Evidence

Commits

Contributors

Files

Active weeks

TestsCI/CDREADMELicenseContributing

Repository

Language

Python

Stars

Forks

License

MIT