The present study proposes a two-stage Bi-Fidelity Deep Neural Network surrogate model to
quantify the uncertainty of structural analysis using low-fidelity data samples added to the …

To achieve multi-response prediction and reliability evaluation of complex structural system,
a high efficient and precision strategy, namely synchronous surrogate modeling-based …

To alleviate the computational overhead of high-dimensional reliability analysis, a cost-
effective surrogate model called PPCK is proposed by combining partial least squares (PLS) …

Due to its capacity to increase system reliability, cold standby redundancy design has
attracted considerable attention and has been applied in crucial-safety engineering systems …

This paper presents the latest improvements introduced in Version 4 of the UQpy,
Uncertainty Quantification with Python, library. In the latest version, the code was …

Along with the continuous progress of lithium-ion batteries and the automotive industry, the
safety of battery-pack systems (BPSs) is gradually becoming a hot topic of concern for …

A method based on a Bi-fidelity Kriging model is proposed for structural reliability analysis. It
is based on adding low-fidelity data samples to the model to predict high-fidelity values, thus …

According to the concept of limit-state margin probability function, a new look-ahead
learning function called stepwise margin reduction (SMR) is proposed for active learning …

This study presents an importance sampling formulation based on adaptively relaxing
parameters from the indicator function and/or the probability density function. The …

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 …