Accelerating PDE-constrained Inverse Solutions with Deep Learning and Reduced Order Models

Inverse problems are pervasive mathematical methods in inferring knowledge from observational and experimental data by leveraging simulations and models. Unlike direct inference methods, inverse problem approaches typically require many forward model solves usually governed by Partial Differential E...

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Veröffentlicht in:	arXiv.org 2019-12
Hauptverfasser:	Sheriffdeen, Sheroze, Ragusa, Jean C, Morel, Jim E, Adams, Marvin L, Bui-Thanh, Tan
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Computer simulation Deep learning Inverse problems Machine learning Mathematical models Model accuracy Neural networks Parameterization Partial differential equations Reduced order models Subspaces Transport equations
Online-Zugang:	Volltext
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