pdebench/PDEBench resource
PDEBench: An Extensive Benchmark for Scientific Machine Learning observed · 2026-08-28
Health v2 · maintenance only
56/100
- Activity 74
- Release rhythm 8
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1553
- days_rel: n/a
- days_push: 156
- n_releases_24m: 0
Adoption not part of the score
1191 stars · 154 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
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
dataset · maturity active
benchmarking machine-learning simulation data-generation deep-learning machine-learning simulation artificial-intelligence python pde scientific-machine-learning neural-operators pinn navier-stokes fluid-dynamics benchmark-suite jax pytorch neurips-2022 physics scientific-computing linux gpu
7 sources
- readme: https://github.com/pdebench/PDEBench · fetched 2026-08-28 · 2c93c544a140
- homepage: https://arxiv.org/abs/2210.07182 · fetched 2026-08-29 · 684c128afc4c
- site_page: https://info.arxiv.org/about/donate.html · fetched 2026-08-29 · cca9c3a11c56
- site_page: https://info.arxiv.org/about/ourmembers.html · fetched 2026-08-29 · 47cbc55ff1de
- site_page: https://info.arxiv.org/about · fetched 2026-08-29 · a1f16f915a9a
- registry_pypi: https://pypi.org/pypi/pdebench/json · fetched 2026-08-29 · bf8bc9ca275d
- site_page: https://info.arxiv.org/labs/index.html · fetched 2026-08-29 · b14a8d05a0ec
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| pdebench/PDEBench | main | 56 |
For agents
markdown · JSON · MCP: product_card(name="pdebench/PDEBench")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem