Augmented Gaussian random field: theory and computation
From MaRDI portal
Publication:2129158
DOI10.3934/dcdss.2021098zbMath1492.60091arXiv2009.01961OpenAlexW3197640410MaRDI QIDQ2129158
Xiu Yang, Guang Lin, Sheng Zhang, Samy Tindel
Publication date: 22 April 2022
Published in: Discrete and Continuous Dynamical Systems. Series S (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2009.01961
Cites Work
- Unnamed Item
- Bayesian solution uncertainty quantification for differential equations
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Machine learning of linear differential equations using Gaussian processes
- Gaussian process modeling with inequality constraints
- Physics-informed cokriging: a Gaussian-process-regression-based multifidelity method for data-model convergence
- Theory of probability and random processes.
- Bayesian monotone regression using Gaussian process projection
- Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations
- Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction
- The Geometry of Random Fields
- Multi-fidelity optimization via surrogate modelling
- Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints
- Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
- Real Analysis and Probability
- 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
- Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression
- When Bifidelity Meets CoKriging: An Efficient Physics-Informed MultiFidelity Method
- Bayesian Probabilistic Numerical Methods
- Probabilistic numerics and uncertainty in computations
This page was built for publication: Augmented Gaussian random field: theory and computation