发表机构
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对仿真到实验微调导致仿真性能下降的问题，提出联合训练多目标学习方法，在RealPDEBench四个流体系统上实现最佳平衡预测并保留仿真知识。
AI 中文摘要
仿真和实验测量为学习时空物理系统提供了互补的数据，但标准的仿真到实验微调在迁移后仅优化实验目标，可能会降低仿真性能。我们将仿真-实验预测表述为一个多目标学习问题，包含特定领域的仿真和实验风险。在来自RealPDEBench的四个流体系统和两种模型容量上，我们比较了仅仿真、仅实验、Sim→Exp和联合训练，并在两个保留域上评估每个最终模型。Sim→Exp倾向于更强烈地特化于实验数据，以牺牲仿真域遗忘为代价。联合训练在广泛的仿真-实验评估权重范围内始终实现最佳平衡性能，同时相比Sim→Exp显著提高了仿真保留度。联合训练还更好地保留了实验测量中缺失的仅仿真字段。项目页面：此https URL。
英文摘要
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.
