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.
Topics covered include, but are not limited to:
- Parameter and topology optimization
- Uncertainty quantification
- Compressible fluid flows and shock waves
- Micro and nano-electromechanical systems (MEMS/NEMS)
- Wind turbines and bladed disc systems
- Dynamics-based machine learning
- Mechanics-informed machine learning
- Model reduction using spectral submanifolds
- Adaptive and localized reduced models
- Closure modeling
- Data-driven dynamical system identification
Guest Editors
Cecilia Pagliantini – Eindhoven University of Technology, Netherlands
Shobhit Jain – Delft University of Technology, Netherlands