Gaussian Process Modeling Using the Principle of Superposition
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Publication:6621637
DOI10.1080/00401706.2018.1473799MaRDI QIDQ6621637
Guilin Li, Matthias Hwai Yong Tan
Publication date: 18 October 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
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- An analysis of metal forming processes using large deformation elastic- plastic formulations
- Bayesian emulation of complex multi-output and dynamic computer models
- A practical guide to splines
- The design and analysis of computer experiments.
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Machine learning of linear differential equations using Gaussian processes
- Computer experiments with functional inputs and scalar outputs by a norm-based approach
- Identification of piecewise constant sources in non-homogeneous media based on boundary measurements
- Convergence study of the truncated Karhunen–Loeve expansion for simulation of stochastic processes
- Estimating Shape Constrained Functions Using Gaussian Processes
- An Introduction to Computational Stochastic PDEs
- Monotone Emulation of Computer Experiments
- Partial Differential Equations and the Finite Element Method
- Spectral Methods for Uncertainty Quantification
- Ten Lectures on Wavelets
- Radial Basis Functions
- Stochastic finite element methods for partial differential equations with random input data
- Identification of point sources in two-dimensional advection-diffusion-reaction equation: application to pollution sources in a river. Stationary case
- Mechanistic Hierarchical Gaussian Processes
- Green’s Functions with Applications, Second Edition
- Gaussian Process Modeling of a Functional Output with Information from Boundary and Initial Conditions and Analytical Approximations
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