Reduced Order Methods for Modeling and Computational Reduction

1 W. H. A. Schilders, A. Lutowska: A novel approach to model order reduction for coupled multiphysics problems.- 2 A. C. Ionita, A. C. Antoulas: Case study. Parametrized Reduction using Reduced-Basis and the Loewner Framework.- 3 M. Bebendorf, Y. Maday, B. Stamm: Comparison of some reduced representation approximations.- 4 H. Antil, M. Heinkenschloss, D. C. Sorensen: Application of the Discrete Empirical Interpolation Method to Reduced Order Modeling of Nonlinear and Parametric System.- 5 K. Urban, S. Volkwein, O. Zeeb: Greedy Sampling using Nonlinear Optimization.- 6 P. Benner, L. Feng: A Robust Algorithm for Parametric Model Order Reduction based on Implicit Moment Matching.- 7 F. Chen, J. S. Hesthaven, X. Zhu: On the use of reduced basis methods to accelerate and stabilize the Parareal method.- 8 C. Farhat, D. Amsallem: On the stability of reduced-order linearized computational fluid dynamics models based on POD and Galerkin projection: descriptor vs non-descriptor forms.- 9 T. Lassila, A. Manzoni, A. Quarteroni, G. Rozza: Model Order Reduction in Fluid Dynamics: Challenges and Perspectives.- 10 L. Grinberg, M. Deng, A. Yakhot, G. Karniadakis: Window Proper Orthogonal Decomposition. Application to Continuum and Atomistic Data.- 11 M. Bergmann, T. Colin, A. Iollo, D. Lombardi, O. Saut, H. Telib: Reduced order models at work in Aeronautics and Medicine.