Data-Driven Evolutionary Modeling in Materials Technology

Released on by Nirupam Chakraborti
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  • Data-Driven Evolutionary Modeling in Materials Technology Book Detail

  • Author : Nirupam Chakraborti
  • Release Date : 2022-09-15
  • Publisher : CRC Press
  • Genre : Technology & Engineering
  • Pages : 507
  • ISBN 13 : 1000635864
  • File Size : 4,4 MB
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Data-Driven Evolutionary Modeling in Materials Technology by Nirupam Chakraborti PDF Summary

Book Description: 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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Data-Driven Evolutionary Optimization

Data-Driven Evolutionary Optimization

File Size : 78,78 MB
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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Wr