A Bandit-Learning Approach to Multifidelity Approximation

DOI10.1137/21M1408312zbMath1478.62007arXiv2103.15342OpenAlexW3150190863MaRDI QIDQ5022495

Robert M. Kirby, Yiming Xu, Vahid Keshavarzzadeh, Akil C. Narayan

Publication date: 19 January 2022

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.15342

zbMATH Keywords

consistencyMonte Carlolinear regressionmultifidelitybandit learning

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Statistical aspects of big data and data science (62R07)

Related Items (2)

Budget-limited distribution learning in multifidelity problems ⋮ Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

Uses Software

• LIBSVM

Cites Work

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