Projection-based model reduction: Formulations for physics-based machine learning

•New approach for physics-based machine learning using POD expansions.•Machine learning methods learn map between inputs and POD coefficients.•Particular solutions in the POD expansion embed physical constraints in ML models.•Particular solutions enforce boundary conditions and other physical soluti...

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Bibliographic Details
Published in:	Computers & fluids Vol. 179; pp. 704 - 717
Main Authors:	Swischuk, Renee, Mainini, Laura, Peherstorfer, Benjamin, Willcox, Karen
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier Ltd 30.01.2019 Elsevier BV
Subjects:	Artificial intelligence Boundary conditions Constraint modelling Data-driven reduced models Decision trees Forecasting Formulations Machine learning Model reduction Neural networks Parameterization Physics-based machine learning Polynomials Proper Orthogonal Decomposition Surrogate models Thermal expansion Physics-based machine learning Proper orthogonal decomposition Model reduction Surrogate models Data-driven reduced models
ISSN:	0045-7930, 1879-0747
Online Access:	Get full text
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