Reduced Order Modeling of Fluid Flows

Dear Colleagues,

The ever-increasing need for computational efficiency and improved accuracy of many applications in fluids leads to very large-scale dynamical systems, whose simulations and analyses make excessive and unmanageable demands on computational resources. Since the computational cost of traditional full-order numerical simulations is extremely prohibitive, many successful model order reduction approaches have been introduced. The purpose of such approaches is to reduce this computational burden and serve as surrogate models for efficient computational analysis fluid systems, especially in settings where the traditional methods require repeated model evaluations over a large range of parameter values. Simplifying computational complexity of the underlying mathematical model, these reduced order models offer promises in many prediction, identification, design, optimization, and control applications. However, they are neither robust with respect to the parameter changes nor low-cost to handle nonlinear dependence for complex nonlinear dynamical systems with temporal and stochastic parameters. Therefore, reduced order modeling remains an open challenge and the development of efficient and reliable model order reduction techniques is of paramount importance for both fundamental and applied fluid dynamics research. Topics in this call include, but are not limited to: projection-based approaches, reduced subspace or basis generation methods, regularization algorithms, coarse-grained simulations, data-driven methods, compressive or sparse sampling ideas and their implementations for fast predictive modeling, parameter identification, data assimilation, design, control, optimization and uncertainty quantification problems arising in fluid dynamics applications.

Dr. Omer San
Prof. Dr. Goodarz Ahmadi
Guest Editors

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• parametric model reduction methods

• proper orthogonal decomposition

• dynamic mode decomposition

• Koopman operators

• radial basis functions

• rational interpolation methods

• empirical interpolation methods

• manifold interpolation methods

• balanced truncation

• closure modeling

• regularization algorithms

• scale-aware basis selection

• Krylov subspace methods

• Volterra series

• nonlinear kernel-based methods

• data-driven methods

• spatiotemporal patterns extraction

• predictability quantification

• non-projection based model reduction

• machine learning enabled reduced-order models

• high-dimensional parameter subspace characterization

• tensor techniques

• non-intrusive models

• compressive sensing

• greedy sampling algorithms

• reduced-order adaptive flow controller for fluid flows