The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of
faults (FDD) in traction systems have become an active issue in the transportation area over …

Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

This paper considers the creation of parametric surrogate models for applications in science
and engineering where the goal is to predict high-dimensional output quantities of interest …

Advances in experimental techniques and the ever-increasing fidelity of numerical
simulations have led to an abundance of data describing fluid flows. This review discusses a …

We develop a new method which extends dynamic mode decomposition (DMD) to
incorporate the effect of control to extract low-order models from high-dimensional, complex …

This article is concerned with data-driven realization of fault detection (FD) for nonlinear
dynamic systems. In order to identify and parameterize nonlinear Hammerstein models …

We develop a new generalization of Koopman operator theory that incorporates the effects
of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of …

In this paper, two online schemes for an integrated design of fault-tolerant control (FTC)
systems with application to Tennessee Eastman (TE) benchmark are proposed. Based on …