Data-driven surrogate modeling and benchmarking for process equipment

Data-driven surrogate modeling and benchmarking for process equipment

In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has b...

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Bibliographic Details
Main Authors: Gabriel F. N. Gonçalves, Assen Batchvarov, Yuyi Liu, Yuxin Liu, Lachlan R. Mason, Indranil Pan, Omar K. Matar
Format: Article
Language:English
Published: Cambridge University Press 2020-01-01
Series:Data-Centric Engineering
Subjects:
Computational fluid dynamics
data-driven
machine learning
surrogate models
Online Access:https://www.cambridge.org/core/product/identifier/S2632673620000088/type/journal_article

Internet

https://www.cambridge.org/core/product/identifier/S2632673620000088/type/journal_article

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