Informatics for Materials Science and Engineering
Chapter 5 - Evolutionary Data-Driven Modeling
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Hybrid approach of using bi-objective genetic programming in well control optimization of waterflood management
2023, Geoenergy Science and EngineeringEvolutionary data driven modeling and tri-objective optimization for noisy BOF steel making data
2023, Digital Chemical EngineeringHybrid optimization approach using evolutionary neural network & genetic algorithm in a real-world waterflood development
2022, Journal of Petroleum Science and EngineeringCitation Excerpt :The bi-objective genetic algorithm is applied to carry out a tradeoff between the error of the network training and the complexity of the network. The prey populations surviving after generations of predator attack will then represent the optimum Pareto solutions (Chakraborti, 2013; Li, 2003; Mondal et al., 2011; Pettersson et al., 2007, 2009). EvoNN algorithm was used extensively in materials and manufacturing processes such as blast furnace analyses and optimization of mechanical properties of micro-alloyed steel (Chugh et al., 2017; Laumanns et al., 1998; Li et al., 2007).
Bi-objective optimal design of a damage-tolerant multifunctional battery system
2016, Materials and DesignCitation Excerpt :The Latin hypercube sampling (LHS) method [33] is adopted to achieve uniformity and balance of sampling points in the design window. The metamodels are constructed from the FE results at sampling points using an evolutionary neural network (EvoNN) approach [23,25,27]. Then, after being validated and tested, the metamodels are used to approximate quantities of interest.
A Data Driven Approach to Identify Optimal Thermal Parameters for Finite Element Analysis of Electric-Assisted Deformation Processes
2023, Metals and Materials InternationalHybrid Multi-Objective Optimization Approach in Water Flooding
2022, Journal of Energy Resources Technology, Transactions of the ASME