Data-Driven Evolutionary Modeling in Materials Technology

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.

由於遺傳和演化算法的效能和優化潛力，它們在學習和建模中被廣泛應用，尤其是在大數據相關問題的出現之後。本書介紹了與材料科學領域相關的算法和策略。它討論了通過這些程序創建的目標函數的演化多目標優化程序，並介紹了可用的代碼。涵蓋了從原始金屬生產到材料設計的最新應用。它還描述了混合建模策略，以及其他常見的建模和模擬策略，如分子動力學、細胞自動機等。

特點：
- 侧重於數據驅動的演化建模和優化，包括演化深度學習。
- 包含有關算法及其在材料科學和技術中的應用的詳細信息。
- 討論將演化算法與通用計算策略相結合的混合數據驅動建模。
- 徹底討論了在冶金和材料領域中相關策略的應用。
- 概述了主要的單目標和多目標演化算法。

本書的目標讀者群包括材料科學、數據驅動工程、冶金工程、計算材料科學、結構材料和功能材料的研究人員、專業人士和研究生。

Professor Nirupam Chakraborti was educated in India and USA, receiving his B.Met.E from Jadavpur University, India, followed by an MS from New Mexico Tech, USA and PhD, PhD degrees from University of Washington, Seattle, USA. He joined Indian Institute of Technology, Kanpur as a member of the faculty in 1984 and switched to Indian Institute of Technology, Kharagpur in 2000.

Internationally known for his pioneering work on evolutionary computation in the area of Metallurgy and Materials, globally, Professor Chakraborti was rated among the top 2% highly cited researchers in the Materials area in 2000, as per Scopus records. A former Docent of Åbo Akademi, Finland, former Visiting Professors of Florida International University and POSTECH, Korea, he also taught and conducted research at several other academic institutions in Austria, Brazil, Finland, Germany, Italy and the US. An international symposium, under the KomPlasTech 2019, which is world's longest running conference series in the area of computational materials technology, was organized in Poland in 2019 to honor him. In 2020, an issue of a prominent Taylor of Francis journal, Materials and Manufacturing Processes was dedicated to him as well. In 2021 Indian Institute of Technology, Kharagpur and Indian Institute of Metals, a professional body, also organized another international seminar in his honor.

This book is a culmination of Professor Chakarborti's decades of research and teaching efforts in this area.

教授Nirupam Chakraborti在印度和美國接受教育，他在印度的Jadavpur大學獲得了B.Met.E學位，之後在美國的新墨西哥科技大學獲得了碩士學位，並在美國華盛頓大學獲得了兩個博士學位。他於1984年加入印度理工學院坎普爾分校，並於2000年轉至印度理工學院卡拉格普爾分校。

作為冶金和材料領域進化計算的開創性工作的國際知名學者，根據Scopus的記錄，Chakraborti教授在2000年被評為材料領域中引用率最高的2%研究人員之一。他曾是芬蘭Åbo Akademi的Docent，以及佛羅里達國際大學和POSTECH韓國的訪問教授，並在奧地利、巴西、芬蘭、德國、意大利和美國的其他學術機構教授和進行研究。為了表彰他，2019年在波蘭舉辦了一個國際研討會，該研討會是全球歷史最悠久的計算材料技術領域的會議系列之一。同樣，在2020年，一本知名的Taylor of Francis期刊《Materials and Manufacturing Processes》的一期專題也是以他為主題。在2021年，印度理工學院卡拉格普爾分校和印度金屬學會（一個專業機構）也為他舉辦了另一個國際研討會。

這本書是Chakarborti教授在這個領域數十年的研究和教學努力的結晶。