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• W3194724611 startingPage "445" @default.
• W3194724611 abstract "This article addresses the inference of physics models from data, from the perspectives of inverse problems and model reduction. These fields develop formulations that integrate data into physics-based models while exploiting the fact that many mathematical models of natural and engineered systems exhibit an intrinsically low-dimensional solution manifold. In inverse problems, we seek to infer uncertain components of the inputs from observations of the outputs, while in model reduction we seek low-dimensional models that explicitly capture the salient features of the input–output map through approximation in a low-dimensional subspace. In both cases, the result is a predictive model that reflects data-driven learning yet deeply embeds the underlying physics, and thus can be used for design, control and decision-making, often with quantified uncertainties. We highlight recent developments in scalable and efficient algorithms for inverse problems and model reduction governed by large-scale models in the form of partial differential equations. Several illustrative applications to large-scale complex problems across different domains of science and engineering are provided." @default.
• W3194724611 created "2021-08-30" @default.
• W3194724611 creator A5017431306 @default.
• W3194724611 creator A5049331711 @default.
• W3194724611 date "2021-05-01" @default.
• W3194724611 modified "2023-10-18" @default.
• W3194724611 title "Learning physics-based models from data: perspectives from inverse problems and model reduction" @default.
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