Data-Driven Kernel Designs for Optimized Greedy Schemes: A Machine Learning Perspective-Reference-Cited by-同舟云学术

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Data-Driven Kernel Designs for Optimized Greedy Schemes: A Machine Learning Perspective

Published:2024-02-01 Issue:1 Volume:46 Page:C101-C126
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Wenzel Tizian¹,Marchetti Francesco²^ORCID,Perracchione Emma³

Affiliation:

1. Institute for Applied Analysis and Numerical Simulation, University of Stuttgart, Stuttgart, 70569, Germany.

2. Dipartimento di Matematica “Tullio Levi-Civita,” Università di Padova, Padova, Italy.

3. Dipartimento di Scienze Matematiche Giuseppe Luigi Lagrange, Politecnico di Torino, Torino, Italy.

Funder

Deutsche Forschungsgemeinschaft

BMBF

Programma Operativo Nazionale

Compagnia di San Paolo

Italian Network on Approximation

Approximation Theory and Applications

Italian Mathematical Union

GNCS-INAM

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference41 articles.

1. Learning via variably scaled kernels

2. On the search of the shape parameter in radial basis functions using univariate global optimization methods

3. Active Subspaces

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Classifier-dependent feature selection via greedy methods;Statistics and Computing;2024-07-06

2. Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data;Mathematics;2024-07-05

3. Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling;Lecture Notes in Computer Science;2024

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