When
Where
Speaker: Nicole Aretz, University of Texas - Austin
Title: Nested Operator Inference for data-driven learning of physics-based reduced-order models
Abstract: Highly accurate full-order models are often too expensive computationally to evaluate in predictive, real-time, or many-query applications. Projection-based model order reduction methods exploit the intrinsic low-dimensionality of the full-order solution manifold. These reduced-order models (ROMs) typically achieve significant computational savings while remaining physically interpretable through the governing equations. Operator Inference (OpInf) is a data-driven learning technique to construct projection-based ROMs without accessing the full-order operators. Because the degrees of freedom in the classic OpInf learning problem scale polynomially in the dimension of the reduced space, classic OpInf requires precise regularization to balance the numerical stability of the OpInf learning problem, the structural stability of the learned ROM, and the achieved reconstruction accuracy. Nested OpInf exploits the inherent hierarchy within the reduced space to iteratively construct initial guesses for the OpInf learning problem that prioritize the interactions of the dominant modes. The initial guess computed for any target reduced dimension corresponds to a ROM with provably smaller or equal snapshot reconstruction error than with standard OpInf. We demonstrate the nested OpInf algorithm on a cubic heat conduction problem and a large-scale, parameterized model of the Greenland ice sheet.