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Model Order Reduction for Parameterized Continuum Mechanics

Youngsoo Choi, Lawrence Livermore National Laboratory
Masayuki Yano, University of Toronto
Matthew Zahr, University of Notre Dame
While physical simulation has become an indispensable tool in engineering design and analysis, a number of real-time and many-query applications remain out of reach for classical high-fidelity analysis techniques. Projection-based model reduction is one approach to reduce the computational cost in these applications while controlling the error introduced in the reduction process. In this mini-symposium, we discuss recent developments in model reduction techniques. Topics include, but not limited to nonlinear approximation techniques; high-dimensional problems; hyperreduction methods for nonlinear PDEs; data-driven methods; incorporation of machine-learning techniques; error estimation and adaptivity; and their applications to optimization, feedback control, uncertainty quantification, and inverse problems in fluid and structural dynamics, with an emphasis on large-scale industry-relevant problems. The minisymposium will bring together researchers working on both fundamental and applied aspects of model reduction to provide a forum for discussion, interaction, and assessment of techniques.