Data-Driven Evolutionary Modeling in Materials Technology
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Data-Driven Evolutionary Modeling in Materials Technology
Chakraborti, Nirupam
Taylor & Francis Ltd
10/2024
304
Mole
9781032061740
15 a 20 dias
Descrição não disponível.
1. Introduction
2. Data with random noise and its modeling
3. Nature inspired non-calculus optimization
4. Single-objective evolutionary algorithms
5. Multi-objective evolutionary optimization
6. Evolutionary learning and optimization using Neural Net paradigm
7. Evolutionary learning and optimization using Genetic Programming paradigm
8. The challenge of big data and Evolutionary Deep Learning
9. Software available in public domain and the commercial software
10. Applications in Iron and Steel making
11. Applications in chemical and metallurgical unit processing
12. Applications in Materials Design
13. Applications in Atomistic Materials Design
14. Applications in Manufacturing
15. Miscellaneous Applications
2. Data with random noise and its modeling
3. Nature inspired non-calculus optimization
4. Single-objective evolutionary algorithms
5. Multi-objective evolutionary optimization
6. Evolutionary learning and optimization using Neural Net paradigm
7. Evolutionary learning and optimization using Genetic Programming paradigm
8. The challenge of big data and Evolutionary Deep Learning
9. Software available in public domain and the commercial software
10. Applications in Iron and Steel making
11. Applications in chemical and metallurgical unit processing
12. Applications in Materials Design
13. Applications in Atomistic Materials Design
14. Applications in Manufacturing
15. Miscellaneous Applications
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Random Noise;Optimization;Iron;Big Data;Deep Learning;Materials Processing;Design;Steel Making;Genetic Algorithms;Evolutionary Algorithms;Pareto Front;Cellular Automata;Simple Genetic Algorithm;Bi-objective Optimization;Multi-objective Genetic Algorithm;Training Error;AICc;Multi-Objective Particle Swarm Optimization;ZDT Test Functions;Data Set;SPEA;Pruning Algorithm;Neural Net;Pareto Solutions;Multi-objective Evolutionary Algorithm;GP Tree;Multi-Objective Differential Evolution;RMSE Value;Evolutionary Multi-objective Optimization;Multi-objective PSO;Vice Versa;GP;Pareto Optimum
1. Introduction
2. Data with random noise and its modeling
3. Nature inspired non-calculus optimization
4. Single-objective evolutionary algorithms
5. Multi-objective evolutionary optimization
6. Evolutionary learning and optimization using Neural Net paradigm
7. Evolutionary learning and optimization using Genetic Programming paradigm
8. The challenge of big data and Evolutionary Deep Learning
9. Software available in public domain and the commercial software
10. Applications in Iron and Steel making
11. Applications in chemical and metallurgical unit processing
12. Applications in Materials Design
13. Applications in Atomistic Materials Design
14. Applications in Manufacturing
15. Miscellaneous Applications
2. Data with random noise and its modeling
3. Nature inspired non-calculus optimization
4. Single-objective evolutionary algorithms
5. Multi-objective evolutionary optimization
6. Evolutionary learning and optimization using Neural Net paradigm
7. Evolutionary learning and optimization using Genetic Programming paradigm
8. The challenge of big data and Evolutionary Deep Learning
9. Software available in public domain and the commercial software
10. Applications in Iron and Steel making
11. Applications in chemical and metallurgical unit processing
12. Applications in Materials Design
13. Applications in Atomistic Materials Design
14. Applications in Manufacturing
15. Miscellaneous Applications
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Random Noise;Optimization;Iron;Big Data;Deep Learning;Materials Processing;Design;Steel Making;Genetic Algorithms;Evolutionary Algorithms;Pareto Front;Cellular Automata;Simple Genetic Algorithm;Bi-objective Optimization;Multi-objective Genetic Algorithm;Training Error;AICc;Multi-Objective Particle Swarm Optimization;ZDT Test Functions;Data Set;SPEA;Pruning Algorithm;Neural Net;Pareto Solutions;Multi-objective Evolutionary Algorithm;GP Tree;Multi-Objective Differential Evolution;RMSE Value;Evolutionary Multi-objective Optimization;Multi-objective PSO;Vice Versa;GP;Pareto Optimum
