Data-Driven Evolutionary Modeling in Materials Technology

✍ Scribed by Nirupam Chakraborti

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 319
• Edition: 1
• Category: Library

✦ Synopsis

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

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Author’s Biography
Chapter 1: Introduction
Chapter 2: Data with Random Noise and Its Modeling
2.1 What Is Data-Driven Modeling?
2.2 Noise in the Data
2.3 Mitigating Random Noise in Traditional Manner
2.4 Overfitting and Underfitting Problems
2.5 Intelligent Optimum Models Out of Data with Random Noise
Chapter 3: Nature Inspired Non-Calculus Optimization
3.1 Using Natural and Biological Analogs for Modeling and Optimization
3.2 Replacing a Gradient-based Optimization by Directional Evolutionary Search and Learning
3.3 Binary Encoding and Simple Genetic Algorithms
3.4 The Genetic Operators in Evolutionary Algorithms
3.5 Hamming Cliff and Gray Encoding
3.6 Real Encoding
3.7 Tree Encoding
3.8 Sequence Encoding
3.9 Schema Theorem
Chapter 4: Single-Objective Evolutionary Algorithms
4.1 Preamble
4.2 Simple Genetic Algorithm (SGA)
4.3 Differential Evolution (DE)
4.4 Particle Swarm Optimization (PSO)
4.5 Ant Colony Optimization (ACO)
4.6 Genetic Programming (GP)
4.7 Micro Genetic Algorithm (μ-GA)
4.8 Island Model of Genetic Algorithm
4.9 Messy Genetic Algorithms
4.10 Evolution Strategies (ES)
4.11 Cellular Automata
4.12 Simulated Annealing
4.13 Constraint Handling
4.14 Evolutionary Algorithms as Equation Solvers
4.15 Evolutionary Optimization of Multimodal Functions
Chapter 5: Multi-Objective Evolutionary Optimization
5.1 The Notion of Pareto Optimality
5.2 The Pareto Frontier and its Representation
5.3 Visualization of Pareto Fronts
5.4 Pareto Optimality versus Nash Equilibrium
5.5 Ranking of Non-Dominated Solutions
5.6 Some Special Features of Evolutionary Multi-objective Optimization Algorithms
5.7 Predator–Prey Genetic Algorithm
5.8 Artificial Immune Algorithm
5.9 Multi-objective Particle Swarm Optimization
5.10 Nash Genetic Algorithm
5.11 Algorithms for Handling a Large Number of Objectives
5.12 The Notion of k-optimality
5.13 Reference Vector Evolutionary Algorithm (RVEA)
5.14 Other Prominent Algorithms
Chapter 6: Evolutionary Learning and Optimization Using Neural Net Paradigm
6.1 Learning Through Conventional Neural Net
6.2 Evolutionary Neural Net: The Different Possibilities
6.3 EvoNN Algorithm: The Learning Module
6.4 EvoNN Algorithm: The Module for Assessing Single Variable Response
6.5 EvoNN Algorithm: The Optimization Module
6.6 Pruning Algorithm
Chapter 7: Evolutionary Learning and Optimization Using Genetic Programming Paradigm
7.1 Learning Through Single Objective Genetic Programming
7.2 Learning Through Bi-objective Genetic Programming
7.3 BioGP Algorithm: The Learning Module
7.4 BioGP Algorithm: The Optimization Module
7.5 BioGP Algorithm: The Module for Assessing Single Variable Response
7.6 Some Special Features of BioGP Emphasized
Chapter 8: The Challenge of Big Data and Evolutionary Deep Learning
8.1 The Challenge of Learning from Big Data
8.2 The Concept of Deep Neural Net
8.3 Development of the EvoDN2 Algorithm
Chapter 9: Software Available in Public Domain and the Commercial Software
9.1 Software for Evolutionary Data-Driven Modeling and Optimization
9.2 The Commercial Software modeFRONTIER
9.3 The Commercial Software KIMEME
9.4 Matlab versions of EvoNN, BioGP, and EvoDN2
9.5 Running EvoNN in Matlab
9.6 Running BioGP in Matlab
9.7 Running EvoDN2 in Matlab
9.8 Many-objective Optimization Using cRVEA in Matlab
9.9 Predictions Using EvoNN/EvoDN2/BioGP Models in Matlab
9.10 Graphics Support for Using EvoNN/EvoDN2/BioGP Models in Matlab
9.11 Python versions of EvoNN, BioGP, and EvoDN2
Chapter 10: Applications in Iron and Steel Making
10.1 Evolutionary Computation in Blast Furnace Ironmaking
10.2 Evolutionary Optimization of the Iron Ore Agglomeration Processes
10.3 Evolutionary Optimization of the Charging and Burden Distribution in Blast Furnace
10.4 Evolutionary Optimization of the Blast Furnace Hot Metal Quality
10.5 Evolutionary Optimization of the Blast Furnace Productivity, Emission, and Cost of Operation
10.6 Some Further Analyses of the Si Content Blast Furnace Hot Metal
10.7 Many-objective Optimization of Blast Furnace
10.8 The Need for Using a Number of Evolutionary Algorithms in Tandem in Blast Furnace Optimization
10.9 Some Other Evolutionary Algorithms Based Studies Related to Blast Furnace Iron Making
10.10 Data-Driven Evolutionary Algorithms Applied to the Alternate Processes of Ferrous Production Metallurgy
10.11 Data-Driven Evolutionary Optimization Applied to the Simulation of Integrated Steel Plants
10.12 Data-Driven Evolutionary Studies for Refining of Steel
10.13 Data-driven evolutionary algorithms in electric furnace steel making
10.14 Evolutionary algorithms in continuous casting
10.15 Single Objective Evolutionary Algorithms Based Studies of Continuous Casting
10.16 Multi-Objective Evolutionary Algorithms Based Studies of Continuous Casting
Chapter 11: Applications in Chemical and Metallurgical Unit Processing
11.1 Evolutionary Optimization of Chemical Processing Plants
11.2 Studies on the William and Otto Chemical Plant
11.3 The Process Model for the William and Otto Chemical Plant
11.4 Some More Studies Related to Chemical Technology
11.5 Evolutionary Optimization of Primary Metal Production
11.6 Evolutionary Optimization of Mineral Processing
11.7 Evolutionary Optimization of Aluminum Extraction
11.8 Evolutionary Analysis Applied to the Thermodynamics of Pb-S-O Vapor Phase
11.9 Evolutionary Applied to the Leaching of Ocean Nodules and Low-grade Ores
11.10 A Study on the Supported Liquid Membrane Based Separation
11.11 Miscellaneous Evolutionary Studies in the Area of Hydrometallurgy
11.12 Evolutionary Algorithms in Zone Refining
11.13 Concluding Remarks
Chapter 12: Applications in Materials Design
12.1 Data-Driven Evolutionary Alloy Design
12.2 Evolutionary Design of Superalloys
12.3 Evolutionary Design of Aluminum Alloys
12.4 Evolutionary Design of Steels
12.5 Evolutionary Design of Functional Materials
12.6 Evolutionary Design of Functionally Graded Materials
12.7 Evolutionary Design of Biomaterials
12.8 Evolutionary Design of Phase Change Materials
12.9 Evolutionary Design of Some Emerging and Less Common Materials
Chapter 13: Applications in Atomistic Materials Design
13.1 Data-Driven Evolutionary Atomistic Material Design
13.2 Density Functional Theory
13.3 Tight Binding Approximation
13.4 Molecular Dynamics Simulations
13.5 Empirical Many-Body Potential Energy Functions
13.6 Development of Empirical Many-Body Potentials Using a Data-Driven Evolutionary Approach
13.7 Data-Driven Evolutionary Optimization of Fe–Zn System
13.8 Evolutionary Design of Ionic Materials
13.9 Taylor-Made Evolutionary Design of Materials
Chapter 14: Applications in Manufacturing
14.1 Evolutionary Algorithms in Manufacturing
14.2 Evolutionary Optimization of Rolling Process
14.3 Evolutionary Optimization of Forging
14.4 Evolutionary Optimization of Extrusion
14.5 Evolutionary Optimization in Welding
14.6 Evolutionary Optimization in Sheet Metal Forming
14.7 Evolutionary Optimization in Advanced Particulate Processing
14.8 Evolutionary Optimization of the Heat Treatment Process
14.9 Evolutionary Studies on Microstructure Generation
14.10 Evolutionary Studies on Metal and Non-Metal Cutting
Chapter 15: Miscellaneous Applications
15.1 Evolutionary Algorithms in Specific Applications
15.2 Data-Driven Evolutionary Algorithms applied to Anisotropic Yielding
15.3 Data-Driven Evolutionary Algorithms applied to Battery Design
15.4 Evolutionary Algorithms applied to VLSI Design
15.5 Evolutionary Design of Paper Machine Headbox
15.6 Evolutionary Algorithms in Nucleic Acid Sequence Alignment
15.7 Evolutionary Analysis of the Heat Transfer Process in a Bloom Reheating Furnace
Epilogue
References
Index