Learning physics-based models from data: Perspectives from model reduction
Friday, February 18, 2022 1pm to 2pm
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150 Western Avenue, Allston, MA 02134
https://iacs.seas.harvard.edu/event/iacs-distinguished-lecturer-series-karen-wilcoxRSVP HERE: https://forms.gle/noD1XiwnNyxnxx8r6
Abstract: Operator Inference is a method for learning predictive reduced-order models from data. The method targets the derivation of a reduced-order model of an expensive high-fidelity simulator that solves known governing equations. Rather than learn a generic approximation with weak enforcement of the physics, we learn low-dimensional operators whose structure is defined by the physical problem being modeled. These reduced operators are determined by solving a linear least squares problem, making Operator Inference scalable to high-dimensional problems. The method is entirely non-intrusive, meaning that it requires simulation snapshot data but does not require access to or modification of the high-fidelity source code. For problems where the complexity of the physics does not admit a global low-rank structure, we construct a nonlinear approximation space. This is achieved via clustering to obtain localized Operator Inference models, or by approximation in a quadratic manifold. The methodology is demonstrated on challenging large-scale problems arising in rocket combustion and materials phase-field applications.
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