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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Vydáno v:	Computers & fluids Ročník 179; s. 704 - 717
Hlavní autoři:	Swischuk, Renee, Mainini, Laura, Peherstorfer, Benjamin, Willcox, Karen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier Ltd 30.01.2019 Elsevier BV
Témata:	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
On-line přístup:	Získat plný text
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