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.
Use this if you are developing or evaluating machine learning models for spatiotemporal forecasting of physical phenomena, and you need a robust benchmark dataset that combines real sensor data with high-fidelity simulations.
Not ideal if your primary interest is in simple, non-physical time series forecasting or if you do not work with partial differential equations (PDEs) or complex physical simulations.
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Mar 08, 2026
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