构建仿真模型分布外泛化测试基准，验证无监督迁移有效性
SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts
- 设计四类工业仿真任务构成新基准，覆盖多物理场景
- 在热轧等任务上验证无监督域适应可提升模型泛化能力
- 为工业级仿真模型提供可复现的分布偏移评估标准
神经代理模型在求解偏微分方程时，常因输入条件超出训练分布（如新初始条件或结构尺寸）而性能显著下降。尽管无监督域适应（UDA）在视觉与语言领域广泛应用，但在复杂工程仿真中的应用仍处于空白。本文提出SIMSHIFT基准数据集与评估套件，包含热轧、板材成形、电动机设计和散热器设计四类工业仿真任务，涵盖多样物理过程。我们扩展主流UDA方法至先进神经代理模型，并系统评估其表现。大量实验揭示了分布外建模的挑战，验证了UDA在仿真中的潜力，也暴露了实现鲁棒神经代理在真实工业场景下应对分布偏移的关键难题。代码已开源。
原文摘要 · Abstract (English)
Neural surrogates for Partial Differential Equations (PDEs) often suffer significant performance degradation when evaluated on problem configurations outside their training distribution, such as new initial conditions or structural dimensions. While Unsupervised Domain Adaptation (UDA) techniques have been widely used in vision and language to generalize across domains without additional labeled data, their application to complex engineering simulations remains largely unexplored. In this work, we address this gap through two focused contributions. First, we introduce SIMSHIFT, a novel benchmark dataset and evaluation suite composed of four industrial simulation tasks spanning diverse processes and physics: hot rolling, sheet metal forming, electric motor design and heatsink design. Second, we extend established UDA methods to state-of-the-art neural surrogates and systematically evaluate them. Extensive experiments on SIMSHIFT highlight the challenges of out-of-distribution neural surrogate modeling, demonstrate the potential of UDA in simulation, and reveal open problems in achieving robust neural surrogates under distribution shifts in industrially relevant scenarios. Our codebase is available at https://github.com/psetinek/simshift
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