artificial intelligence and machine learning in numerical simulation /
First Statement of Responsibility
Shahab D. Mohaghegh.
EDITION STATEMENT
Edition Statement
First edition.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Boca Raton, FL :
Name of Publisher, Distributor, etc.
CRC Press,
Date of Publication, Distribution, etc.
2023.
PROJECTED PUBLICATION DATE
Date
2210
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
189p.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
SUMMARY OR ABSTRACT
Text of Note
"Numerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart proxy models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations which can otherwise take tens of hours. This book focuses on smart proxy modeling and provides readers with all the essential details on how to develop smart proxy models using artificial intelligence and machine learning, as well as how it may be used in real-world cases. Covers replication of highly accurate numerical simulations using artificial intelligence and machine learning. Details application in reservoir simulation and modeling, and computational fluid dynamics. Includes real case studies based on commercially available simulators. Smart Proxy Modeling is ideal for petroleum, chemical, environmental, and mechanical engineers, as well as statisticians and others working with applications of data-driven analytics"--