Multi-output Gaussian processes with Bayesian active learning reduce emulation error and parameter uncertainty in hydrodynamic model calibration

Data for the research article: Multi-output Gaussian processes with Bayesian active learning reduce emulation error and parameter uncertainty in hydrodynamic model calibration

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-29
DOI
https://doi.org/10.5281/zenodo.23025722
Primary Topic
Gaussian Processes and Bayesian Inference
Type
article
Field-Weighted Citation Impact
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article

Multi-output Gaussian processes with Bayesian active learning reduce emulation error and parameter uncertainty in hydrodynamic model calibration

Andrés Heredia
Zenodo (CERN European Organization for Nuclear Research)
Gaussian Processes and Bayesian Inference
article

Multi-output Gaussian processes with Bayesian active learning reduce emulation error and parameter uncertainty in hydrodynamic model calibration

Andrés Heredia
article en

Abstract

Data for the research article: Multi-output Gaussian processes with Bayesian active learning reduce emulation error and parameter uncertainty in hydrodynamic model calibration

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 9%
Gaussian Processes and Bayesian Inference
DOIOpen Access PDFOpenAlex
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Multi-output Gaussian processes with Bayesian active learning reduce emulation error and parameter uncertainty in hydrodynamic model calibration — Andrés Heredia · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS