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Showing 1 to 4 of 4 for “"Gaussian process surrogates"”.
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Computer Experimental Design for Gaussian Process Surrogates
… However, some computer experiments for complex processes are still computationally demanding. A surrogate model or emulator, is often employed as a fast substitute for the simulator. Meanwhile, a common challenge in computer experiments and related fields is to efficiently explore the input …
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Deep Gaussian Process Surrogates for Computer Experiments
Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model non-stationary dynamics. Recent applications in machine learning favor approximate, optimization-based inference for fast …
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Efficient computer experiment designs for Gaussian process surrogates
… that can result in a wealth of knowledge. Gaussian processes (GPs) are highly desirable models for computer experiments for their predictive accuracy and uncertainty quantification. This dissertation addresses GP modeling when data abounds as well as GP adaptive design when simulator …
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EMG-Based Human-in-the-Loop Bayesian Optimization to Assist Hip-Centric Activities
… preserving assistance quality. We show that processed EMG provides a reliable objective for rapid personalization, enabling convergence within typical clinical sessions. The research progresses from simulation studies revealing fundamental controller-hardware gaps to experimental validation …