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Data-Driven Evolutionary Modeling in Materials Technology (Hardcover)
Loot Price: R5,092
Discovery Miles 50 920
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Data-Driven Evolutionary Modeling in Materials Technology (Hardcover)
Expected to ship within 12 - 19 working days
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Due to efficacy and optimization potential of genetic and
evolutionary algorithms, they are used in learning and modeling
especially with the advent of big data related problems. This book
presents the algorithms and strategies specifically associated with
pertinent issues in materials science domain. It discusses the
procedures for evolutionary multi-objective optimization of
objective functions created through these procedures and introduces
available codes. Recent applications ranging from primary metal
production to materials design are covered. It also describes
hybrid modeling strategy, and other common modeling and simulation
strategies like molecular dynamics, cellular automata etc.
Features: Focuses on data-driven evolutionary modeling and
optimization, including evolutionary deep learning. Include details
on both algorithms and their applications in materials science and
technology. Discusses hybrid data-driven modeling that couples
evolutionary algorithms with generic computing strategies.
Thoroughly discusses applications of pertinent strategies in
metallurgy and materials. Provides overview of the major single and
multi-objective evolutionary algorithms. This book aims at
Researchers, Professionals, and Graduate students in Materials
Science, Data-Driven Engineering, Metallurgical Engineering,
Computational Materials Science, Structural Materials, and
Functional Materials.
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