# bitzhangcy/Neural-PDE-Solver

Repository: https://github.com/bitzhangcy/Neural-PDE-Solver
Canonical: https://ross.abutalabs.com/products/neural-pde-solver
License Family: other
Last push: 2026-06-29T02:26:22+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 89, release rhythm 35, longevity 99
- inputs: {"age_days": 1398, "days_push": 66, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1120, forks 140 (observed 2026-08-28T04:03:39.606407+00:00)

## What it is
A curated literature repository tracking research on solving partial differential equations with neural operators and related deep learning methods. It organizes papers into categories such as PINN, DeepONet, Fourier operators, graph networks, and benchmarks.

## Use cases
- find papers on neural PDE solvers
- learn about physics-informed neural networks
- survey neural operator methods like DeepONet and FNO
- track recent research in scientific machine learning
- find benchmarks for neural PDE solving
- research inverse problems and inverse design with neural networks

## When to choose
- you need a structured reading list on neural PDE solving
- you are starting research in scientific machine learning or neural operators
- you want to stay current on PINN and DeepONet literature

## When to avoid
- you need runnable solver code or a software library
- you need classical numerical PDE solvers like FEM packages

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: machine-learning, tutorials
- platform: cross-platform
- tags: awesome-list, neural-operators, pde-solver, scientific-machine-learning, pinn, deeponet, fourier-neural-operator, paper-collection, algorithms

## Member repositories
- bitzhangcy/Neural-PDE-Solver (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:39.606407+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 2026-08-30T06:41:05.055619+00:00, confidence not recorded.
  - readme: https://github.com/bitzhangcy/Neural-PDE-Solver (fetched 2026-08-28T04:03:39.606407+00:00, sha 68c4a152b179)
- Data as of 2026-08-30T08:39:29.467469+00:00.