Data-Driven Evolutionary Modeling in Materials Technology
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Data-Driven Evolutionary Modeling in Materials Technology

Data-Driven Evolutionary Modeling in Materials Technology

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About the Book

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.

Table of Contents:
1. Introduction 2. Data with random noise and its modeling 3. Nature inspired non-calculus optimization 4. Single-objective evolutionary algorithms 5. Multi-objective evolutionary optimization 6. Evolutionary learning and optimization using Neural Net paradigm 7. Evolutionary learning and optimization using Genetic Programming paradigm 8. The challenge of big data and Evolutionary Deep Learning 9. Software available in public domain and the commercial software 10. Applications in Iron and Steel making 11. Applications in chemical and metallurgical unit processing 12. Applications in Materials Design 13. Applications in Atomistic Materials Design 14. Applications in Manufacturing 15. Miscellaneous Applications


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Product Details
  • ISBN-13: 9781000635829
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: CRC Press
  • Language: English
  • ISBN-10: 1000635821
  • Publisher Date: 15 Sep 2022
  • Binding: Digital (delivered electronically)


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Data-Driven Evolutionary Modeling in Materials Technology
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Data-Driven Evolutionary Modeling in Materials Technology
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