# idrl-lab/PINNpapers

Must-read Papers on Physics-Informed Neural Networks.

Repository: https://github.com/idrl-lab/PINNpapers
Canonical: https://ross.abutalabs.com/products/pinnpapers
Language: Python
License: MIT
License Family: permissive
Last push: 2023-12-08T21:59:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1850, "days_push": 999, "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 1533, forks 208 (observed 2026-08-28T04:04:59.626534+00:00)

## What it is
A curated reading list of must-read papers on Physics-Informed Neural Networks (PINNs), maintained by the IDRL lab. It organizes representative works by topic (models, parallelization, acceleration, transfer learning, uncertainty quantification, applications, and analysis) and includes a BibTeX-to-markdown conversion script.

## Use cases
- find papers on physics-informed neural networks
- get started learning about PINNs for scientific computing
- survey PINN applications and theory before starting research
- find open-source PINN software libraries like DeepXDE
- keep up with recent PINN acceleration and parallelization work
- build a bibliography for a PINN literature review

## When to choose
- you need a curated, categorized overview of the PINN research landscape
- you are a researcher or student surveying physics-informed deep learning literature
- you want pointers to both papers and accompanying code repositories

## When to avoid
- you need a runnable PINN library rather than a paper list
- you need an exhaustive, automatically updated paper index instead of a curated selection
- you need tutorials or code examples rather than references to papers

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, simulation, documentation
- domain: machine-learning, deep-learning, simulation, tutorials, awesome-lists
- platform: python
- tags: awesome-list, physics-informed-neural-networks, pinn, papers, scientific-computing, reading-list, bibliography

## Member repositories
- idrl-lab/PINNpapers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:59.626534+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 2026-08-30T04:31:15.143495+00:00, confidence not recorded.
  - readme: https://github.com/idrl-lab/PINNpapers (fetched 2026-08-28T04:04:59.626534+00:00, sha f1c191732ac7)
- Data as of 2026-08-30T08:39:29.467469+00:00.