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

翻译：偏微分方程（PDE）的神经代理模型在评估训练分布以外的问题配置（如新的初始条件或结构维度）时，常出现显著的性能下降。尽管无监督域自适应（UDA）技术已在视觉和语言领域被广泛用于在无需额外标注数据的情况下实现跨域泛化，但其在复杂工程仿真中的应用仍基本未被探索。本研究通过两项聚焦性贡献填补了这一空白。首先，我们提出了SIMSHIFT——一个由涵盖不同工艺与物理过程的四个工业仿真任务（热轧、钣金成形、电机设计与散热器设计）构成的新型基准数据集与评估套件。其次，我们将成熟的UDA方法拓展至前沿神经代理模型，并对其进行系统评估。在SIMSHIFT上进行的大量实验揭示了分布外神经代理建模的挑战，展示了UDA在仿真领域的潜力，同时暴露了在工业相关场景中实现分布迁移下鲁棒神经代理模型所面临的开放性问题。我们的代码库已发布于 https://github.com/psetinek/simshift