Title: RealBench: A Benchmark for Complex Physical Systems with Real-World Data
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: complex physical system, PDE, benchmark, real-world data, prediction

Abstract: Predicting the evolution of complex physical systems remains a central problem in science and engineering. Despite rapid progress in scientific Machine Learning (ML) models, a critical bottleneck is the lack of expensive real-world data, resulting in most current models being trained and validated on simulated data. Beyond limiting the development and evaluation of scientific ML, this gap also hinders research into essential tasks such as sim-to-real transfer. We introduce RealBench, the first benchmark that integrates real-world measurements with paired numerical simulations. RealBench consists of five datasets, three tasks, eight metrics, and nine baselines. We first present five real-world measured datasets with paired simulated datasets across different complex physical systems. We further define three tasks, which allow comparisons between real-world and simulated data, and facilitate the development of methods to bridge the two. Moreover, we design eight evaluation metrics, spanning data-oriented and physics-oriented metrics, and finally benchmark nine representative scientific ML baselines, including state-of-the-art models and pretrained PDE foundation models. Experiments reveal significant discrepancies between simulated and real-world data, while showing that pretraining with simulated data consistently improves both accuracy and convergence. In this work, we hope to provide insights from real-world data, advancing scientific ML toward bridging the sim-to-real gap and real-world deployment.

TL;DR: We propose the first benchmark for complex physical systems with paired real-world data and simulated data, and explore how to bridge simulated and real-world data.