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arXiv · 2610.01974

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

Mahindra Rautela·Alexander Scheinker·Ayan Biswas·Diane Oyen·Nathan DeBardeleben·Earl Lawrence
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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: https://mahindrautela.github.io/morph.

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Mahindra Rautela, Alexander Scheinker, Ayan Biswas, Diane Oyen, Nathan DeBardeleben, Earl Lawrence. 2026-10-01. Sim+Real: Joint Simulation - Experiment Training Improves Balanced Prediction in Physical Systems. https://arxiv.org/abs/2610.01974

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