Muutke küpsiste eelistusi

E-raamat: Data-Driven Evolutionary Modeling in Materials Technology [Taylor & Francis e-raamat]

(Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, India.)
  • Formaat: 304 pages, 58 Tables, black and white; 162 Line drawings, black and white; 1 Halftones, black and white; 163 Illustrations, black and white
  • Ilmumisaeg: 15-Sep-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003201045
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 244,66 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 349,51 €
  • Säästad 30%
Data-Driven Evolutionary Modeling in Materials TechnologySuurem pilt
  • Formaat: 304 pages, 58 Tables, black and white; 162 Line drawings, black and white; 1 Halftones, black and white; 163 Illustrations, black and white
  • Ilmumisaeg: 15-Sep-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003201045
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003201045
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.
Preface xi
Author's Biography xiii
Chapter 1 Introduction
1(2)
Chapter 2 Data with Random Noise and Its Modeling
3(4)
2.1 What Is Data-Driven Modeling?
3(1)
2.2 Noise in the Data
3(1)
2.3 Mitigating Random Noise in Traditional Manner
4(1)
2.4 Overfitting and Underfitting Problems
4(1)
2.5 Intelligent Optimum Models Out of Data with Random Noise
5(2)
Chapter 3 Nature Inspired Non-Calculus Optimization
7(14)
3.1 Using Natural and Biological Analogs for Modeling and Optimization
7(1)
3.2 Replacing a Gradient-based Optimization by Directional Evolutionary Search and Learning
7(1)
3.3 Binary Encoding and Simple Genetic Algorithms
8(1)
3.4 The Genetic Operators in Evolutionary Algorithms
9(5)
3.5 Hamming Cliff and Gray Encoding
14(2)
3.6 Real Encoding
16(1)
3.7 Tree Encoding
16(1)
3.8 Sequence Encoding
16(2)
3.9 Schema Theorem
18(3)
Chapter 4 Single-Objective Evolutionary Algorithms
21(28)
4.1 Preamble
21(1)
4.2 Simple Genetic Algorithm (SGA)
21(3)
4.3 Differential Evolution (DE)
24(3)
4.4 Particle Swarm Optimization (PSO)
27(2)
4.5 Ant Colony Optimization (ACO)
29(1)
4.6 Genetic Programming (GP)
30(4)
4.7 Micro Genetic Algorithm (μ-GA)
34(1)
4.8 Island Model of Genetic Algorithm
35(1)
4.9 Messy Genetic Algorithms
36(2)
4.10 Evolution Strategies (ES)
38(2)
4.11 Cellular Automata
40(2)
4.12 Simulated Annealing
42(1)
4.13 Constraint Handling
42(2)
4.14 Evolutionary Algorithms as Equation Solvers
44(1)
4.15 Evolutionary Optimization of Multimodal Functions
44(5)
Chapter 5 Multi-Objective Evolutionary Optimization
49(22)
5.1 The Notion of Pareto Optimality
49(4)
5.2 The Pareto Frontier and its Representation
53(1)
5.3 Visualization of Pareto Fronts
54(1)
5.4 Pareto Optimality versus Nash Equilibrium
55(1)
5.5 Ranking of Non-Dominated Solutions
56(1)
5.6 Some Special Features of Evolutionary Multi-Objective Optimization Algorithms
57(1)
5.7 Predator-Prey Genetic Algorithm
57(3)
5.8 Artificial Immune Algorithm
60(2)
5.9 Multi-Objective Particle Swarm Optimization
62(2)
5.10 Nash Genetic Algorithm
64(1)
5.11 Algorithms for Handling a Large Number of Objectives
65(1)
5.12 The Notion of k-optimality
66(1)
5.13 Reference Vector Evolutionary Algorithm (RVEA)
67(1)
5.14 Other Prominent Algorithms
68(3)
Chapter 6 Evolutionary Learning and Optimization Using Neural Net Paradigm
71(18)
6.1 Learning Through Conventional Neural Net
71(2)
6.2 Evolutionary Neural Net: The Different Possibilities
73(1)
6.3 EvoNN Algorithm: The Learning Module
74(8)
6.4 EvoNN Algorithm: The Module for Assessing Single Variable Response
82(1)
6.5 EvoNN Algorithm: The Optimization Module
83(2)
6.6 Pruning Algorithm
85(4)
Chapter 7 Evolutionary Learning and Optimization Using Genetic Programming Paradigm
89(12)
7.1 Learning Through Single Objective Genetic Programming
89(1)
7.2 Learning Through Bi-objective Genetic Programming
89(3)
7.3 BioGP Algorithm: The Learning Module
92(5)
7.4 BioGP Algorithm: The Optimization Module
97(1)
7.5 BioGP Algorithm: The Module for Assessing Single Variable Response
97(2)
7.6 Some Special Features of BioGP Emphasized
99(2)
Chapter 8 The Challenge of Big Data and Evolutionary Deep Learning
101(10)
8.1 The Challenge of Learning from Big Data
101(1)
8.2 The Concept of Deep Neural Net
101(1)
8.3 Development of the EvoDN2 Algorithm
102(9)
Chapter 9 Software Available in Public Domain and the Commercial Software
111(24)
9.1 Software for Evolutionary Data-Driven Modeling and Optimization
111(1)
9.2 The Commercial Software mode FRONTJER
111(1)
9.3 The Commercial Software KIMEME
112(6)
9.4 Matlab versions of EvoNN, BioGP, and EvoDN2
118(5)
9.5 Running EvoNN in MATLAB
123(2)
9.6 Running BioGP in MATLAB
125(1)
9.7 Running EvoDN2 in MATLAB
125(2)
9.8 Many-objective Optimization using cRVEA in MATLAB
127(2)
9.9 Predictions Using EvoNN/EvoDN2/BioGP Models in MATLAB
129(3)
9.10 Graphics Support for using EvoNN/EvoDN2/BioGP Models in MATLAB
132(1)
9.11 Python Versions of EvoNN, BioGP, and EvoDN2
133(2)
Chapter 10 Applications in Iron and Steel Making
135(42)
10.1 Evolutionary Computation in Blast Furnace Ironmaking
135(1)
10.2 Evolutionary Optimization of the Iron Ore Agglomeration Processes
136(1)
10.3 Evolutionary Optimization of the Charging and Burden Distribution in Blast Furnace
137(2)
10.4 Evolutionary Optimization of the Blast Furnace Hot Metal Quality
139(1)
10.5 Evolutionary Optimization of the Blast Furnace Productivity, Emission, and Cost of Operation
140(4)
10.6 Some Further Analyses of the Si Content Blast Furnace Hot Metal
144(3)
10.7 Many-objective Optimization of Blast Furnace
147(2)
10.8 The Need for Using a Number of Evolutionary Algorithms in Tandem in Blast Furnace Optimization
149(6)
10.9 Some Other Evolutionary Algorithms Based Studies Related to Blast Furnace Iron Making
155(1)
10.10 Data-Driven Evolutionary Algorithms Applied to the Alternate Processes of Ferrous Production Metallurgy
155(3)
10.11 Data-Driven Evolutionary Optimization applied to the Simulation of Integrated Steel Plants
158(5)
10.12 Data-Driven Evolutionary Studies for Refining of Steel
163(2)
10.13 Data-Driven Evolutionary Algorithms in Electric Furnace Steel Making
165(1)
10.14 Evolutionary Algorithms in Continuous Casting
166(1)
10.15 Single Objective Evolutionary Algorithms Based Studies of Continuous Casting
167(7)
10.16 Multi-Objective Evolutionary Algorithms Based Studies of Continuous Casting
174(3)
Chapter 11 Applications in Chemical and Metallurgical Unit Processing
177(18)
11.1 Evolutionary Optimization of Chemical Processing Plants
177(1)
11.2 Studies on the William and Otto Chemical Plant
177(1)
11.3 The Process Model for the William and Otto Chemical Plant
177(4)
11.4 Some More Studies Related to Chemical Technology
181(1)
11.5 Evolutionary Optimization of Primary Metal Production
181(1)
11.6 Evolutionary Optimization of Mineral Processing
181(3)
11.7 Evolutionary Optimization of Aluminum Extraction
184(1)
11.8 Evolutionary Analysis Applied to the Thermodynamics of Pb-S-O Vapor Phase
185(2)
11.9 Evolutionary Applied to the Leaching of Ocean Nodules and Low-grade Ores
187(3)
11.10 A Study on the Supported Liquid Membrane Based Separation
190(2)
11.11 Miscellaneous Evolutionary Studies in the area of Hydrometallurgy
192(1)
11.12 Evolutionary Algorithms in Zone Refining
193(1)
11.13 Concluding Remarks
194(1)
Chapter 12 Applications in Materials Design
195(22)
12.1 Data-Driven Evolutionary Alloy Design
195(1)
12.2 Evolutionary Design of Superalloys
195(5)
12.3 Evolutionary Design of Aluminum Alloys
200(1)
12.4 Evolutionary Design of Steels
201(7)
12.5 Evolutionary Design of Functional Materials
208(1)
12.6 Evolutionary Design of Functionally Graded Materials
209(1)
12.7 Evolutionary Design of Biomaterials
210(1)
12.8 Evolutionary Design of Phase Change Materials
211(1)
12.9 Evolutionary Design of Some Emerging and Less Common Materials
211(6)
Chapter 13 Applications in Atomistic Materials Design
217(20)
13.1 Data-Driven Evolutionary Atomistic Material Design
217(1)
13.2 Density Functional Theory
217(1)
13.3 Tight Binding Approximation
218(1)
13.4 Molecular Dynamics Simulations
219(1)
13.5 Empirical Many-Body Potential Energy Functions
220(2)
13.6 Development of Empirical Many-Body Potentials Using a Data-Driven Evolutionary Approach
222(2)
13.7 Data-Driven Evolutionary Optimization of Fe-Zn System
224(9)
13.8 Evolutionary Design of Ionic Materials
233(1)
13.9 Taylor-Made Evolutionary Design of Materials
233(4)
Chapter 14 Applications in Manufacturing
237(24)
14.1 Evolutionary Algorithms in Manufacturing
237(1)
14.2 Evolutionary Optimization of Rolling Process
237(7)
14.3 Evolutionary Optimization of Forging
244(1)
14.4 Evolutionary Optimization of Extrusion
244(1)
14.5 Evolutionary Optimization in Welding
245(1)
14.6 Evolutionary Optimization in Sheet Metal Forming
245(2)
14.7 Evolutionary Optimization in Advanced Particulate Processing
247(5)
14.8 Evolutionary Optimization of the Heat Treatment Process
252(5)
14.9 Evolutionary Studies on Microstructure Generation
257(2)
14.10 Evolutionary Studies on Metal and Non-Metal Cutting
259(2)
Chapter 15 Miscellaneous Applications
261(8)
15.1 Evolutionary Algorithms in Specific Applications
261(1)
15.2 Data-Driven Evolutionary Algorithms applied to Anisotropic Yielding
261(2)
15.3 Data-Driven Evolutionary Algorithms applied to Battery Design
263(1)
15.4 Evolutionary Algorithms applied to VLSI Design
264(1)
15.5 Evolutionary Design of Paper Machine Headbox
264(1)
15.6 Evolutionary Algorithms in Nucleic Acid Sequence Alignment
264(2)
15.7 Evolutionary Analysis of the Heat Transfer Process in a Bloom Reheating Furnace
266(3)
Epilogue 269(2)
References 271(26)
Index 297
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 worlds 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 Chakarbortis decades of research and teaching efforts in this area.
Püsilink: https://www.kriso.ee/db/9781003201045_pe.html
Märksõnad: