Pages that link to "Item:Q2162525"
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The following pages link to Quantifying uncertainty with ensembles of surrogates for blackbox optimization (Q2162525):
Displaying 9 items.
- Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for~computationally expensive black-box global optimization problems (Q486382) (← links)
- Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise (Q1668809) (← links)
- Order-based error for managing ensembles of surrogates in mesh adaptive direct search (Q1704921) (← links)
- Black-box combinatorial optimization using models with integer-valued minima (Q2043440) (← links)
- Surrogate-based distributed optimisation for expensive black-box functions (Q2663899) (← links)
- High-dimensional black-box optimization under uncertainty (Q2669612) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- Surrogate-Based Ensemble Grouping Strategies for Embedded Sampling-Based Uncertainty Quantification (Q5141287) (← links)
- Surrogate‐based methods for black‐box optimization (Q5278217) (← links)
