PDEBench and CFDBench
These are complements that address different scopes of the same problem: PDEBench provides a broad benchmark across multiple PDE types including fluid dynamics, while CFDBench specializes in large-scale fluid dynamics benchmarks with potentially deeper domain-specific scenarios, allowing practitioners to validate ML models across both general PDE solving and fluid-specific challenges.
About PDEBench
pdebench/PDEBench
PDEBench: An Extensive Benchmark for Scientific Machine Learning
This project provides a comprehensive benchmark for evaluating machine learning models designed to solve Partial Differential Equations (PDEs). It offers a wide range of realistic physical problems, along with ready-to-use datasets containing various initial/boundary conditions and PDE parameters. Scientists, engineers, and researchers working with scientific machine learning can use this to compare and develop methods for simulating complex physical phenomena.
About CFDBench
luo-yining/CFDBench
A large-scale benchmark for machine learning methods in fluid dynamics
This project provides a comprehensive collection of fluid dynamics simulations to help researchers and engineers evaluate how well machine learning models can predict fluid behavior. It includes various scenarios like fluid flowing through tubes or around objects, with different conditions and geometries. If you're developing or testing machine learning models for computational fluid dynamics (CFD), this benchmark helps you assess their generalizability across diverse real-world problems.
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