用D-最优统计量稳定高维仿真代理的测试时适应，提升泛化性能。
Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics
- 基于D-最优统计量存储，实现测试时稳定自适应
- 在高维回归任务中提升7%的分布外性能
- 适合仿真加速与生成设计优化场景
机器学习代理模型被广泛用于加速工程中的昂贵仿真，但训练与部署间的分布偏移常导致性能严重下降（如未见几何形状或配置）。测试时适应（TTA）可缓解此类偏移，但现有方法主要针对低维分类任务，依赖结构化输出和视觉对齐的输入输出关系，难以适用于常见于仿真的高维、无结构回归问题。本文提出一种基于存储最大信息量（D-最优）统计量的TTA框架，实现测试时稳定适应与参数选择的统一。应用于预训练仿真代理模型时，该方法在几乎零计算开销下实现最高7%的分布外性能提升。据我们所知，这是首个系统性验证高维仿真回归与生成设计优化中有效TTA的工作，已在SIMSHIFT与EngiBench基准上完成验证。
原文摘要 · Abstract (English)
Machine learning surrogates are increasingly used in engineering to accelerate costly simulations, yet distribution shifts between training and deployment often cause severe performance degradation (e.g., unseen geometries or configurations). Test-Time Adaptation (TTA) can mitigate such shifts, but existing methods are largely developed for lower-dimensional classification with structured outputs and visually aligned input-output relationships, making them unstable for the high-dimensional, unstructured and regression problems common in simulation. We address this challenge by proposing a TTA framework based on storing maximally informative (D-optimal) statistics, which jointly enables stable adaptation and principled parameter selection at test time. When applied to pretrained simulation surrogates, our method yields up to 7% out-of-distribution improvements at negligible computational cost. To the best of our knowledge, this is the first systematic demonstration of effective TTA for high-dimensional simulation regression and generative design optimization, validated on the SIMSHIFT and EngiBench benchmarks.
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