Nonlinear model reduction from equations and data
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Modeling in applied science and engineering targets increasingly ambitious objectives, which typically yield increasingly complex models. Despite major advances in computations, simulating such models with exceedingly high dimensions remains a challenge. Even if technically feasible, numerical simulations on such high-dimensional problems do not necessarily give the simplified insight into these phenomena that motivated their initial models. Reduced-order models hold more promise for a quick assessment of changes under parameters and uncertainties, as well as for effective prediction and control. Such models are also highly desirable for systems that are only known in the form of data sets. This focus issue will survey the latest trends in nonlinear model reduction for equations and data sets across various fields of applications, ranging from computational to theoretical aspects.