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

2026-10-01

AI Summary

This study addresses the catastrophic forgetting and prediction imbalance arising from simulation-to-experiment fine-tuning by proposing a multi-objective joint training paradigm. Specifically, simulation and experimental predictions are formulated as a multi-objective optimization task, where domain adaptation and spatiotemporal physical system modeling are leveraged to jointly optimize risks across both domains, effectively overcoming the limitations of conventional sequential fine-tuning. Evaluated on tasks such as fluid dynamics, the proposed method achieves optimal balanced performance under diverse weight configurations. It significantly enhances cross-domain retention capabilities while substantially improving simulation-domain performance without compromising features inherent to the scarce experimental data.

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.

Problem

• spatiotemporal physical systems
• multi-objective learning
• simulation-experiment prediction
• domain forgetting
• balanced performance

Innovation

• Multi-objective learning
• Joint training
• Simulation-to-experiment transfer
• Catastrophic forgetting
• Spatiotemporal physical systems