A Kronecker product accelerated efficient sparse Gaussian process (E-SGP) for flow emulation

DOI10.1016/j.jcp.2024.113460MaRDI QIDQ6648366

M. J. Bluck, Yu Duan, Matthew D. Eaton

Publication date: 4 December 2024

Published in: Journal of Computational Physics (Search for Journal in Brave)

zbMATH Keywords

Gaussian processesBayesian machine learningstructured \& unstructured fluid mechanics datasetsvariational energy free Gaussian process

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Inference from stochastic processes (62Mxx) Stochastic processes (60Gxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Machine learning of linear differential equations using Gaussian processes
• Solving and learning nonlinear PDEs with Gaussian processes
• Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
• Sparse On-Line Gaussian Processes
• Gaussian processes for time-series modelling
• Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations
• Machine Learning for Fluid Mechanics
• Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

This page was built for publication: A Kronecker product accelerated efficient sparse Gaussian process (E-SGP) for flow emulation