Gradient-enhanced deep Gaussian processes for multifidelity modeling

DOI10.1016/j.jcp.2024.113474MaRDI QIDQ6648379

Ingo Jahn, Peter M. Dower, Kieran Mackle, Viv Bone, Chris van der Heide, Chris Manzie

Publication date: 4 December 2024

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

zbMATH Keywords

Gaussian process regressionsurrogate modelinggradient data

Mathematics Subject Classification ID

Statistics (62-XX) Artificial intelligence (68Txx) Stochastic processes (60Gxx)

Cites Work

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• Aerodynamic design via control theory
• Optimal design for kernel interpolation: applications to uncertainty quantification
• Bayesian optimization using deep Gaussian processes with applications to aerospace system design
• Kernels for Vector-Valued Functions: A Review
• Multi-fidelity optimization via surrogate modelling
• Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
• Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
• Predicting the output from a complex computer code when fast approximations are available
• RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY
• Universal co-kriging under intrinsic coregionalization

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