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2019
An ensemble of surrogate models (EM) is a surrogate model composed of a series of surrogate models combined through a weighted sum. An EM can take advantage of each individual surrogate model to effectively increase the robustness of the prediction. The mathematical expression for an EM can be given as follows:
Ping Jiang, Qi Zhou, Xinyu Shao
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An ensemble of surrogate models (EM) is a surrogate model composed of a series of surrogate models combined through a weighted sum. An EM can take advantage of each individual surrogate model to effectively increase the robustness of the prediction. The mathematical expression for an EM can be given as follows:
Ping Jiang, Qi Zhou, Xinyu Shao
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Transfer learning based variable-fidelity surrogate model for shell buckling prediction
, 2021K. Tian +4 more
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2019
One critical aspect before assessing the geotechnical system deformation or stability conditions (responses) is the determination of the limit state surface numerically represented by limit state functions (or performance functions). In many complicated and nonlinear problems where the analyses involve the use of numerical procedures such as the finite
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One critical aspect before assessing the geotechnical system deformation or stability conditions (responses) is the determination of the limit state surface numerically represented by limit state functions (or performance functions). In many complicated and nonlinear problems where the analyses involve the use of numerical procedures such as the finite
openaire +1 more source
Surrogate-Agent Modeling for Improved Training
2014Computer-aided practice can help improve personnel training for demanding scenarios in terms of time and quality. In this paper, we concentrate on asymmetrical conflicts, such as a unit that deals with hostile crowds robbing a store, with the aim of preventing further criminal activity and at the same time minimizing physical and emotional damage.
Ales Tavcar +4 more
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Optimization with Surrogate Models
2013In this chapter, we show how artificial curiosity can be used to focus on the most pertinent search points in black-box optimization. We present a novel response surface method, which employs a memory-based model to estimate the interestingness of each candidate point using Gaussian process regression.
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