With massive advancements in sensor technologies and Internet-of-things (IoT), we now
have access to terabytes of historical data; however, there is a lack of clarity on how to best …

To deal with the time-dependent reliability sensitivity (TDRS) analysis of rare events with
both random variables and stochastic processes, a single-loop sampling procedure …

Time-variant reliability sensitivity analysis aims to measure the effect of input variables on
the time-variant failure probability, which can then be used to guide structural design and …

This article presents a data-driven control strategy for nonlinear dynamical systems,
enabling the construction of a Koopman-based linear system associated with nonlinear …

Machine learning has witnessed substantial growth, leading to the development of
advanced artificial intelligence models crafted to address a wide range of real-world …

Scientific machine learning has seen significant progress with the emergence of operator
learning. However, existing methods encounter difficulties when applied to problems on …

The first-passage problem plays a significant role in engineering performance evaluation
and design optimization. To address general stochastic dynamical systems, a data-driven …

Assessment of imprecise time-variant reliability in engineering is a critical task when
accounting for both the variability of structural properties and loads over time and the …

Operator learning frameworks have recently emerged as an effective scientific machine
learning tool for learning complex nonlinear operators of differential equations. Since neural …

The data-driven approaches based on the Koopman operator theory have promoted the
analysis and control of the nonlinear dynamics by providing an equivalent Koopman-based …