Pages that link to "Item:Q2182781"
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The following pages link to Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (Q2182781):
Displaying 12 items.
- Mid-range metamodel assembly building based on linear regression for large scale optimization problems (Q381741) (← links)
- Adaptive surrogate-based harmony search algorithm for design optimization of variable stiffness composite materials (Q2021910) (← links)
- Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization (Q2022061) (← links)
- Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization (Q2070360) (← links)
- Data-driven construction of convex region surrogate models (Q2358021) (← links)
- Initialization of metaheuristics: comprehensive review, critical analysis, and research directions (Q6056893) (← links)
- Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization (Q6074062) (← links)
- Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
- Adaptive approximation-based multi-objective hybrid optimization method for dual-gradient top-hat structures (Q6151478) (← links)
- A subset-selection-based derivative-free optimization algorithm for dynamic operation optimization in a steel-making process (Q6151482) (← links)
- Dynamic exploration-exploitation Pareto approach for high-dimensional expensive black-box optimization (Q6568408) (← links)
- Global and preference-based optimization with mixed variables using piecewise affine surrogates (Q6667570) (← links)
