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Abstract: It is known that incorporating gradient information can significantly enhance the prediction accuracy of stochastic kriging.
ABSTRACT. It is known that incorporating gradient information can significantly enhance the prediction accuracy of stochastic kriging.
We extend this methodology to the setting where gradient estimators are available, thereby making the gradient-enhanced stochastic kriging metamodel scalable.
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This paper rethink the original formulation of stochastic kriging SK to allow for the flexibility of utilizing different estimation methods for metamodel ...
A Scalable Approach to Enhancing Stochastic Kriging with Gradients · Sample and Computationally Efficient Stochastic Kriging in High Dimensions · Surrogate-Based ...
Oct 22, 2024 · We introduce an approach for enhancing stochastic kriging in the setting where additional direct gradient information is available (e.g., ...
A Scalable Approach to Enhancing Stochastic Kriging with Gradients (2018), Proceedings of 51st Winter Simulation Conference (WSC), pp. 2213-2224, with H ...
We propose a simple, effective approach to improve the performance of stochastic kriging ... A Scalable Approach to Enhancing Stochastic Kriging with Gradients ...
We show theoretically, through some simplified models, that incorporating gradient estimators into stochastic kriging tends to significantly improve surface ...
Missing: Scalable | Show results with:Scalable
Mar 7, 2018 · The key in this approach is that it identifies a general functional form of covariance functions that can induce sparsity in the ...