Reduced Order Methods for Modeling and Computational Reduction

Chapter 1 A Novel Approach to Model Order Reduction for Coupled Multiphysics Problems

Chapter 2 Case Study: Parametrized Reduction Using Reduced-Basis and the Loewner Framework

Chapter 3 Comparison of Some Reduced Representation Approximations

Chapter 4 Application of the Discrete Empirical Interpolation Method to Reduced Order Modeling of Nonlinear and Parametric Systems

Chapter 5 Greedy Sampling Using Nonlinear Optimization

Chapter 6 A Robust Algorithm for Parametric Model Order Reduction Based on Implicit Moment Matching

Chapter 7 On the Use of Reduced Basis Methods to Accelerate and Stabilize the Parareal Method

Chapter 8 On the Stability of Reduced-Order Linearized Computational Fluid Dynamics Models Based on POD and Galerkin Projection: Descriptor vs Non-Descriptor Forms

Chapter 9 Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

Chapter 10 Window Proper Orthogonal Decomposition: Application to Continuum and Atomistic Data