  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.
