Data-Driven Evolutionary Modeling in Materials Technology

Data-Driven Evolutionary Modeling in Materials Technology
English | 2022 | ISBN: 9781003201045 | 319 pages | True PDF | 41.36 MB
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.