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Apr 18, 2024 · In this paper, we compare different multi-fidelity methods employed in constructing Gaussian process surrogates for regression.
Apr 18, 2024 · In this paper, we compare different multi-fidelity methods employed in constructing Gaussian process surrogates for regression. Non-linear ...
1 day ago · One of the main challenges in surrogate modeling is the limited availability of data due to resource constraints associated with ...
In this paper, we compare different multi-fidelity methods employed in constructing Gaussian process surrogates for regression. Non-linear autoregressive ...
The paper reviews different multi-fidelity GP surrogate modeling approaches, including linear auto-regressive models, non-linear auto-regressive models, and ...
A widely used MF surrogate modeling technique is co-kriging [2], [3], in which vector-valued Gaussian processes are used for the regression with multiple data ...
Leveraging Multi-fidelity Gaussian Process Surrogate Modeling for Efficient Regression in Physics Simulations. Multi-fidelity methods can effectively ...
The Gaussian Process (GP)-based surrogate model will not be very accurate when we have limited high-fidelity (experimental) data.
Apr 16, 2024 · Review: Gaussian Process-based surrogate models This section provides a brief overview of GP and MF methods relevant to this study. 2.1 ...
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