# pdebench/PDEBench

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Repository: https://github.com/pdebench/PDEBench
Canonical: https://ross.abutalabs.com/products/pdebench
Homepage: https://arxiv.org/abs/2210.07182
Language: Python
License: NOASSERTION
License Family: other
Topics: ai, benchmark, jax, machine-learning, pytorch, scientific, scientific-computing, sciml, simulation, deep-learning, fluid-dynamics, navier-stokes-equations, neural-networks, neural-operators, partial-differential-equations, physics-informed-neural-networks, autoregressive-models
Last push: 2026-03-30T08:34:19+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 74, release rhythm 8, longevity 100
- inputs: {"age_days": 1553, "days_push": 156, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1191, forks 154 (observed 2026-08-28T04:03:56.159690+00:00)

## What it is
PDEBench is a benchmark suite for scientific machine learning consisting of code and large ready-to-use datasets of time-dependent partial differential equation (PDE) simulation tasks. It includes data generation tools, pretrained baselines (FNO, U-Net, PINN, gradient-based inverse methods), and an extensible API for evaluating new ML models against classical numerical solvers.

## Use cases
- benchmark my new neural operator model on PDE simulation tasks
- download ready-to-use datasets of Navier-Stokes and other PDE solutions
- train FNO or U-Net baselines on scientific ML problems
- evaluate machine learning models for forward and inverse PDE problems
- generate PDE simulation datasets with varied initial and boundary conditions
- compare my physics-informed neural network against standard baselines
- find a standardized benchmark for scientific machine learning research

## When to choose
- you need standardized, challenging PDE benchmarks with large precomputed datasets
- you want to compare a new SciML model against established baselines like FNO, U-Net, or PINN
- you need both forward and inverse problem tasks across a wide range of PDEs
- you want an extensible benchmark with a user-friendly API for the SciML community

## When to avoid
- you need a general-purpose numerical PDE solver rather than an ML benchmark
- you require a lightweight dataset for quick prototyping on limited compute
- you need guaranteed compatibility with the very latest Python/JAX/PyTorch versions for all components
- your domain is not physics simulation or PDE-based modeling

## Facets
- artifact type: dataset
- maturity: active
- function: benchmarking, machine-learning, simulation, data-generation, deep-learning
- domain: machine-learning, simulation, artificial-intelligence
- platform: python
- tags: pde, scientific-machine-learning, neural-operators, pinn, navier-stokes, fluid-dynamics, benchmark-suite, jax, pytorch, neurips-2022, physics, scientific-computing, linux, gpu

## Member repositories
- pdebench/PDEBench (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:56.159690+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 2026-08-30T06:22:42.876063+00:00, confidence not recorded.
  - readme: https://github.com/pdebench/PDEBench (fetched 2026-08-28T04:03:56.159690+00:00, sha 2c93c544a140)
  - homepage: https://arxiv.org/abs/2210.07182 (fetched 2026-08-29T12:30:22.436921+00:00, sha 684c128afc4c)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T12:30:22.445895+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T12:30:22.451181+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T12:30:22.453277+00:00, sha a1f16f915a9a)
  - registry_pypi: https://pypi.org/pypi/pdebench/json (fetched 2026-08-29T12:30:22.454874+00:00, sha bf8bc9ca275d)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T12:30:22.448821+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.