# nschloe/awesome-scientific-computing

:sunglasses: Curated list of awesome software for numerical analysis and scientific computing

Repository: https://github.com/nschloe/awesome-scientific-computing
Canonical: https://ross.abutalabs.com/products/awesome-scientific-computing
Language: Python
License: CC0-1.0
License Family: permissive
Topics: awesome, awesome-list, mathematics, numerical-analysis, physics, engineering, scientific-computing
Last push: 2026-07-20T10:32:58+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 3040, "days_push": 44, "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 1583, forks 182 (observed 2026-08-28T04:05:07.177019+00:00)

## What it is
A curated awesome-list of software and resources for scientific computing and numerical analysis, covering linear algebra, PDE solvers, finite elements, meshing, and visualization. It is a reference catalog (CC0-licensed) rather than a usable library itself.

## Use cases
- find libraries for solving PDEs
- discover open-source finite element packages
- compare sparse linear solver options
- find meshing and mesh generation tools
- locate scientific visualization software
- find BLAS/LAPACK alternatives for dense linear algebra
- discover Python scientific computing packages

## When to choose
- you need to survey the landscape of numerical/scientific computing software before picking a tool
- you want a maintained, community-curated starting point for numerical analysis resources
- you are building a course or reading list on scientific computing

## When to avoid
- you need an actual numerical library or solver rather than a list of links
- you need detailed benchmarks or comparisons between the listed tools
- you need machine learning or statistics resources outside numerical analysis

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, math, data-science
- domain: mathematics, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, scientific-computing, numerical-analysis, curated-list, finite-elements, linear-algebra, pde-solvers, meshing, algorithms

## Member repositories
- nschloe/awesome-scientific-computing (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.177019+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 2026-08-30T03:56:01.469318+00:00, confidence not recorded.
  - readme: https://github.com/nschloe/awesome-scientific-computing (fetched 2026-08-28T04:05:07.177019+00:00, sha 028916033214)
- Data as of 2026-08-30T08:39:29.467469+00:00.