PHYSICS-BASED COVARIANCE MODELS FOR GAUSSIAN PROCESSES WITH MULTIPLE OUTPUTS
From MaRDI portal
Publication:5045397
DOI10.1615/Int.J.UncertaintyQuantification.2012003722MaRDI QIDQ5045397
Emil M. Constantinescu, Mihai Anitescu
Publication date: 4 November 2022
Published in: International Journal for Uncertainty Quantification (Search for Journal in Brave)
Gaussian processes (60G15) General nonlinear regression (62J02) Analysis of variance and covariance (ANOVA) (62J10)
Related Items (7)
Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical model outputs ⋮ A Physics-Based Emulator for the Simulation of Geophysical Mass Flows ⋮ Deep Gaussian process for multi-objective Bayesian optimization ⋮ Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices ⋮ Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows ⋮ Modeling Tangential Vector Fields on a Sphere ⋮ Multivariate versus univariate Kriging metamodels for multi-response simulation models
This page was built for publication: PHYSICS-BASED COVARIANCE MODELS FOR GAUSSIAN PROCESSES WITH MULTIPLE OUTPUTS