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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.
Preface
About the author
Artificial Intelligence and Machine Learning
Machine learning
Artificial neural networks
Deep learning
Fuzzy clustering
Featuring generation
Partitioning
Note
Numerical simulation and modeling
Numerical reservoir simulation (NRS)
Computational fluid dynamics (CFD)
Proxy modeling
Traditional proxy modeling
Reduced order model (ROM)
Response surface method (RSM)
Smart proxy modeling
Smart Proxy Modeling for numerical reservoir simulation
Well-based smart proxy modeling
Cell-based smart proxy modeling
Smart Proxy Modeling for computational fluid dynamics (CFD)
References
Index