Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics
type: Item
instance of: scholarly article
title: Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics
author: Yanyan Li
published in: Journal of Computational Physics
publication date: 2022-10-11T00:00:00Z
full work available at URL: https://arxiv.org/abs/2206.02563
Mathematics Subject Classification ID: 94Axx
Mathematics Subject Classification ID: 60Hxx
Mathematics Subject Classification ID: 65Cxx
zbMATH DE Number: 7599627
zbMATH Keywords: reproducing kernel Hilbert space
zbMATH Keywords: Gaussian process regression
zbMATH Keywords: kernel ridge regression
zbMATH Keywords: kernel flow
zbMATH Keywords: aerodynamics
Wikidata QID: Q114163178
describes a project that uses: PyTorch
describes a project that uses: PDCO
describes a project that uses: KernSmooth
describes a project that uses: elsA
describes a project that uses: Adam
describes a project that uses: SPGL1
MaRDI profile type: Publication
OpenAlex ID: W4294957454
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DOI: 10.1016/J.JCP.2022.111595
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