Optimal design for kernel interpolation: applications to uncertainty quantification
DOI10.1016/j.jcp.2020.110094OpenAlexW3114107221MaRDI QIDQ2124880
Liang Yan, Tao Zhou, Akil C. Narayan
Publication date: 11 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.06291
Hermite interpolationuncertainty quantificationFekete pointskernel interpolationCholesky decomposition with pivoting
Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Approximations and expansions (41Axx)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Multi-output local Gaussian process regression: applications to uncertainty quantification
- High-dimensional adaptive sparse polynomial interpolation and applications to parametric PDEs
- A non-adapted sparse approximation of PDEs with stochastic inputs
- Interpolation of scattered data: distance matrices and conditionally positive definite functions
- An alternative procedure for selecting a good value for the parameter \(c\) in RBF-interpolation
- Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation
- Adaptive greedy techniques for approximate solution of large RBF systems
- Doubly stochastic radial basis function methods
- An adaptive greedy algorithm for solving large RBF collocation problems
- Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree
- An algorithm for selecting a good value for the parameter \(c\) in radial basis function interpolation
- Sparse grid collocation schemes for stochastic natural convection problems
- On choosing ``optimal shape parameters for RBF approximation
- Interpolation of spatial data – A stochastic or a deterministic problem?
- Kernel-based Approximation Methods using MATLAB
- A Christoffel function weighted least squares algorithm for collocation approximations
- Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction
- On the Instability Issue of Gradient-Enhanced Gaussian Process Emulators for Computer Experiments
- Kernel techniques: From machine learning to meshless methods
- An improved subspace selection algorithm for meshless collocation methods
- An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data
- Weighted Approximate Fekete Points: Sampling for Least-Squares Polynomial Approximation
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- Constructing Least-Squares Polynomial Approximations
- Sparse Approximation of Data-Driven Polynomial Chaos Expansions: An Induced Sampling Approach
- Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation
- A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions
- STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION
- High-Order Collocation Methods for Differential Equations with Random Inputs
- Scattered Data Approximation
This page was built for publication: Optimal design for kernel interpolation: applications to uncertainty quantification