This paper presents a physics and data co-driven surrogate modeling method for efficient rare event simulation of civil and mechanical systems with high-dimensional input uncertainties. The method fuses interpretable low-fidelity physical models with data-driven error corrections. The hypothesis is that a well-designed and well-trained simplified physical model can preserve salient features of the original model, while data-fitting techniques can fill the remaining gaps between the surrogate and original model predictions. The coupled physics-data-driven surrogate model is adaptively trained using active learning, aiming to achieve a high correlation and small bias between the surrogate and original model responses in the critical parametric region of a rare event. A final importance sampling step is introduced to correct the surrogate model-based probability estimations. Static and dynamic problems with input uncertainties modeled by random field and stochastic process are studied to demonstrate the proposed method.

翻译：本文提出了一种物理与数据协同驱动的代理建模方法，用于高效模拟具有高维输入不确定性的土木与机械系统中的稀有事件。该方法将可解释的低保真物理模型与数据驱动的误差校正相融合。其核心假设是：设计良好且训练充分的简化物理模型能够保留原始模型的核心特征，而数据拟合技术可填补代理模型与原始模型预测之间的剩余差距。该耦合物理-数据驱动代理模型采用主动学习进行自适应训练，旨在使代理模型与原始模型在稀有事件关键参数区域内的响应具有高相关性及低偏差。最后引入重要性采样步骤对基于代理模型的概率估计进行校正。通过输入不确定性由随机场和随机过程建模的静态与动态问题，验证了所提方法的有效性。