Data-Driven Evolutionary Modeling in Materials Technology
eBook - ePub

Data-Driven Evolutionary Modeling in Materials Technology

  1. 304 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data-Driven Evolutionary Modeling in Materials Technology

About this 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.

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Yes, you can access Data-Driven Evolutionary Modeling in Materials Technology by Nirupam Chakraborti in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Arithmetic. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2022
Print ISBN
9781032061733
eBook ISBN
9781000635867
Edition
1
Topic
Technology & Engineering
Subtopic
Arithmetic
Index
Technology & Engineering

Table of contents

  1. Cover Page
  2. Half Title page
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Contents
  7. Preface
  8. Author’s Biography
  9. 1 Introduction
  10. 2 Data with Random Noise and Its Modeling
  11. 3 Nature Inspired Non-Calculus Optimization
  12. 4 Single-Objective Evolutionary Algorithms
  13. 5 Multi-Objective Evolutionary Optimization
  14. 6 Evolutionary Learning and Optimization Using Neural Net Paradigm
  15. 7 Evolutionary Learning and Optimization Using Genetic Programming Paradigm
  16. 8 The Challenge of Big Data and Evolutionary Deep Learning
  17. 9 Software Available in Public Domain and the Commercial Software
  18. 10 Applications in Iron and Steel Making
  19. 11 Applications in Chemical and Metallurgical Unit Processing
  20. 12 Applications in Materials Design
  21. 13 Applications in Atomistic Materials Design
  22. 14 Applications in Manufacturing
  23. 15 Miscellaneous Applications
  24. Epilogue
  25. References
  26. Index