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替代模型方法，用于高效模拟具有高维输入不确定性的土木和机械系统中的罕见事件。该方法将可解释的低保真度物理模型与数据驱动的误差校正相融合。其假设为：经过合理设计与训练的简化物理模型能够保留原模型的主要特征，而数据拟合技术则可填补替代模型与原模型预测之间的剩余差距。采用主动学习对耦合的物理-数据驱动替代模型进行自适应训练，旨在在罕见事件的关键参数区域实现替代模型与原模型响应之间的高相关性和小偏差。最后引入重要性采样步骤以校正基于替代模型所得的概率估计。通过研究采用随机场和随机过程建模输入不确定性的静态与动态问题，验证了所提出的方法。