PDEBench

PDEBench (2023) is a systematic PDE benchmark platform that provides a unified evaluation environment for data-driven solvers and physics-informed neural networks (PINN). Compared with earlier scattered benchmarks, it is orders of magnitude larger in scale (several TB), diversity (20+ equation types) and task difficulty (low- to high-dimensional), making it the closest thing to a "PDE ImageNet" among public datasets.

Covered equations

• ODE: stochastic differential equations, delayed systems
• PDE: heat, wave, advection-diffusion, Navier-Stokes, shallow water, Burgers
• Multiphysics: heat-fluid coupling, reaction-diffusion

Specs

• Total size: ~5 TB
• Training samples: 10,000+ per equation
• Resolution: 128x128, 256x256, 512x512
• Format: HDF5 (.h5) with initial/boundary conditions and reference solutions
• License: Apache-2.0

Use cases: Training and evaluating surrogate models; ablation studies of PINN / DeepONet / FNO; fair comparison of new architectures; teaching the difference between PDE numerical and data-driven methods.

Note: Download from the official GitHub Release (or S3). The paper should be cited as Takamoto et al., PDEBench, 2023.