Sim+Real: Joint Simulation - Experiment Training Improves Balanced Prediction in Physical Systems

Abstract:Simulation and experimental measurements provide complementary data for learning spatiotemporal physical systems, but standard simulation-to-experiment fine-tuning optimizes only the experimental objective after transfer and can degrade simulation performance. We formulate simulation--experiment prediction as a multi-objective learning problem with domain-specific simulation and experimental risks. On four fluid systems from RealPDEBench and two model capacities, we compare Simulation only, Experiment only, Sim$\rightarrow$Exp, and Joint training, evaluating every final model on both held-out domains. Sim$\rightarrow$Exp tends to specialize more strongly to experimental data at the cost of simulation-domain forgetting. Joint training consistently achieves the best balanced performance over a broad range of simulation--experiment evaluation weightings, while substantially improving simulation retention over Sim$\rightarrow$Exp. Joint also better preserves simulation-only fields absent from experimental measurements. Project page: this https URL.

Machine Learning (cs.LG)

arXiv:2610.01974 [cs.LG]

(or arXiv:2610.01974v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.01974 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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