PDEBench and RealPDEBench
These are complementary benchmarks that address different scopes: PDEBench focuses on diverse synthetic PDE problems for ML model evaluation, while RealPDEBench extends this paradigm by introducing paired real-world and simulated data for validating ML methods on complex physical systems, making them useful together for comprehensive evaluation from synthetic to realistic settings.
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 RealPDEBench
AI4Science-WestlakeU/RealPDEBench
[ICLR26 Oral] RealPDEBench: A Benchmark for Complex Physical Systems with Paired Real-World and Simulated Data
This project helps scientists and engineers working with complex physical systems to forecast future states of these systems more accurately. It provides a benchmark dataset with both real-world measurements and matched numerical simulations, which can be used to train and evaluate machine learning models. Researchers in fluid dynamics, combustion, and other simulation-heavy fields can use this to develop better predictive models.
Related comparisons
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