Data-driven evolutionary modeling in materials technology. (2022)

• Record Type: Book
• Further Information: Note: Nirupam Chakraborti.
• Authors: Chakraborti, Nirupam
• Contents: 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 analogues 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); 4.11Cellular Automata; 4.12 Simulated Annealing; 4.13 Constraint handling; 4.14 Evolutionary algorithms as equation solver; 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 vs 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 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 Few 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 some 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;
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 620.110285; Materials science -- Data processing; Materials science -- Mathematical models
• Languages: English
• ISBNs: 9781000635867; 9781000635829; 9781003201045
• Related ISBNs: 9781032061733
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