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Nonlinear Model Reduction From Equations and Data

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.

Guest Editors: Cecilia Pagliantini and Shobhit Jain

Special Collection Image
Thomas Thurnher; George Haller; Shobhit Jain
Aihui Liu; Joar Axås; George Haller
Rudy Geelen; Laura Balzano; Stephen Wright; Karen Willcox
K. Chand; H. Rosenberger; B. Sanderse
R. Bandy; R. Morrison
Nicola Rares Franco; Stefania Fresca; Filippo Tombari; Andrea Manzoni
Samuel E. Otto; Gregory R. Macchio; Clarence W. Rowley
Rodrigo Singh; Wayne Isaac Tan Uy; Benjamin Peherstorfer
Di Qi; Jian-Guo Liu
Andrew Cleary; Jacob Page
Wenhui Tan; Hao Tian; Yanjie Song; Xiaojun Duan
Guangwei Wang; Guanrong Chen; Hai-Tao Zhang
George Haller; Bálint Kaszás; Aihui Liu; Joar Axås
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