⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

September 29, 2026Open Access

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

Andrés Heredia

View Full Paper

Ask AI

Bookmark

Share

Abstract

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

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Andrés Heredia (2026) studied this question.

synapsesocial.com/papers/6abdf85666f3754618516c7b https://doi.org/10.5281/zenodo.23025721

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Optimization of 2d hydrodynamic model roughness with multioutput Gaussian process emulators and Bayesian active learningOriginal Research · 2026
2Surrogate-Assisted Global Calibration of a Three-Dimensional Hydrodynamic Lake Model Using Machine LearningOriginal Research · 2026
3Gradient-based active learning with Gaussian processes for global sensitivity analysis.Original Research · 2026
4Deep Reinforcement Multiagent Learning Framework for Information Gathering with Local Gaussian Processes for Water Monitoring2024 · 6 citations
5Multioutput Gaussian Processes for Loss Ratio DevelopmentOriginal Research · 2025 · 1 citations

More related papers

Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models2018
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models2024
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes2024