# maziarraissi/PINNs

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

Repository: https://github.com/maziarraissi/PINNs
Canonical: https://ross.abutalabs.com/products/pinns
Homepage: https://maziarraissi.github.io/PINNs
Language: Python
License: MIT
License Family: permissive
Last push: 2026-02-11T23:24:47+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 67, release rhythm 35, longevity 100
- inputs: {"age_days": 3146, "days_push": 203, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6111, forks 1608 (observed 2026-08-28T04:09:35.960518+00:00)

## What it is
The original reference implementation of Physics-Informed Neural Networks (PINNs), which train neural networks to solve and discover nonlinear partial differential equations while respecting physical laws. It provides continuous-time and discrete-time models for data-driven PDE solutions and parameter discovery, written in Python with TensorFlow 1.x.

## Use cases
- solve partial differential equations with neural networks
- discover PDE coefficients from data
- build physics-informed surrogate models
- learn PINNs from the original paper's code
- run forward and inverse PDE problems with deep learning
- reproduce Raissi et al. PINN benchmark results

## When to choose
- you want the canonical reference implementation accompanying the original PINN papers
- you are studying or extending PINN research and need the exact algorithms from the 2017-2019 publications
- you need data-driven solution or discovery of nonlinear PDEs in a TensorFlow 1.x setting

## When to avoid
- you need an actively maintained library - the repo is explicitly no longer maintained
- you use modern PyTorch, JAX, or TensorFlow 2 - the authors recommend pinns-torch, pinns-jax, or pinns-tf2 instead
- you need production-grade, well-tested scientific software rather than research code

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, simulation, math
- domain: machine-learning, deep-learning, simulation, mathematics, artificial-intelligence
- platform: python
- tags: physics-informed-neural-networks, pde-solver, scientific-computing, surrogate-modeling, tensorflow, research-code

## Member repositories
- maziarraissi/PINNs (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:35.960518+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:48:11.496266+00:00, confidence not recorded.
  - readme: https://github.com/maziarraissi/PINNs (fetched 2026-08-28T04:09:35.960518+00:00, sha 86fb2aa6ce8a)
  - homepage: https://maziarraissi.github.io/PINNs (fetched 2026-08-29T08:44:42.992102+00:00, sha d3349b8261a4)
- Data as of 2026-08-30T08:39:29.467469+00:00.