发表机构
South Dakota School of Mines and Technology; University of California, Los Angeles（南达科他矿业理工学院; 加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过重用高保真加载路径更新神经算子，在运行条件变化时恢复精度并改善指令选择，同时指出预测特定误差评估是可信决策的关键。
AI 中文摘要
神经算子可以加速重复的非线性力学计算，但当运行条件超出训练范围时，其精度可能会下降。本研究探讨了在使用过程中获得的高保真解是否可以用于自适应调整神经算子，并改进后续基于力学的指令选择。使用高保真有限元（FE）模型模拟了两个硬磁软材料系统，为评估代理预测和所选指令提供了参考解。神经算子根据已知的材料、载荷和磁场输入预测变形，而经验误差估计器确定哪些预测可用于指令选择。选定的有限元评估补充这些预测，并保留其完整的加载路径，用于定期更新神经算子和估计器。在两个示例中，当刚度和载荷超出训练范围时，固定算子损失了大量精度。使用16条获取的路径进行更新，恢复了大部分丢失的精度，同时在标称范围内保持了精度。在相同的有限元评估预算下，更新后的算子也改善了指令选择，尽管收益随运行条件而变化。误差估计的一致性较差，有时接受不准确的预测，而拒绝准确的预测。这些结果表明，重用高保真加载路径可以扩展神经算子的有效运行范围。然而，仅提高前向精度并不能保证可靠的预测接受，这突显了针对特定预测的误差评估是可信决策的独立要求。
英文摘要
Neural operators can accelerate repeated nonlinear mechanics calculations, but their accuracy can deteriorate as operating conditions move beyond the training range. This work studies whether high-fidelity solutions acquired during use can be reused to adapt a neural operator and improve subsequent mechanics-based command selection. Two hard-magnetic soft-material systems are simulated using high-fidelity finite-element (FE) models, providing reference solutions for evaluating surrogate predictions and selected commands. A neural operator predicts deformation from known material, loading, and magnetic-field inputs, while an empirical error estimator determines which predictions may be used for command selection. Selected FE evaluations supplement these predictions, and their complete loading paths are retained for periodic updates of the neural operator and estimator. In both examples, the fixed operator loses substantial accuracy when stiffness and loading move outside the training range. Updates using 16 acquired paths recover much of the lost accuracy while preserving accuracy in the nominal regime. Under the same FE evaluation budget, the updated operators also improve command selection, although the benefit varies with the operating condition. Error estimation is less consistent, with inaccurate predictions sometimes accepted and accurate predictions rejected. These results demonstrate that reusing high-fidelity loading paths can extend the useful operating range of a neural operator. However, improved forward accuracy alone does not guarantee reliable prediction acceptance, highlighting prediction-specific error assessment as a separate requirement for trustworthy decision making.
Comments33 pages, 16 figures
