Jun 25, 2021 · We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the ...
We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound ...
Deterministic global optimization with Gaussian processes embedded. Schweidtmann, Artur M.; Bongartz, Dominik; Grothe, Daniel; Kerkenhoff, Tim; Lin, Xiaopeng ...
We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and ...
We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound ...
A reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded, which reduces computational time by orders of ...
Jan 18, 2022 · Schweidtmann, Artur M.; Bongartz, Dominik; Grothe, Daniel; Kerkenhoff, Tim; Lin, Xiaopeng; Najman, Jaromił; Mitsos, Alexander.
Deterministic global optimization with Gaussian processes embedded. Author: Schweidtmann, Artur M. Bongartz, Dominik ; Grothe, Daniel ; Kerkenhoff, Tim ; Lin ...
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May 21, 2020 · Our contribution focuses on the deterministic global solution of optimization problems with trained GPs embedded and on applications in process ...
We introduce a novel Bayesian approach to global optimiza- tion using Gaussian processes. We frame the optimization of both noisy and noiseless functions as ...