Kernel-Based Approximation Methods for Partial Differential Equations: Deterministic or Stochastic Problems?
DOI10.1007/978-3-319-59912-0_19zbMath1385.65012OpenAlexW2737513277MaRDI QIDQ4609813
Publication date: 26 March 2018
Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-59912-0_19
numerical experimentsHermite-Birkhoff interpolationGaussian process regressionpositive definite kernelkernel-based approximation methodkernel-based probability spacemultiple Poisson equation
Laplace operator, Helmholtz equation (reduced wave equation), Poisson equation (35J05) Stochastic partial differential equations (aspects of stochastic analysis) (60H15) PDEs with randomness, stochastic partial differential equations (35R60) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)
Related Items (4)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Optimal designs of positive definite kernels for scattered data approximation
- A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters
- Approximation of nonlinear stochastic partial differential equations by a kernel-based collocation method
- Interpolation of spatial data. Some theory for kriging
- Kernel-Based Collocation Methods Versus Galerkin Finite Element Methods for Approximating Elliptic Stochastic Partial Differential Equations
- Interpolation of spatial data – A stochastic or a deterministic problem?
- A Kernel-Based Collocation Method for Elliptic Partial Differential Equations With Random Coefficients
- Kernel-based Approximation Methods using MATLAB
- Accurate Uncertainty Quantification Using Inaccurate Computational Models
- Support Vector Machines
- Gaussian Hilbert Spaces
- Conditional Confidence Statements and Confidence Estimators
- Radial Basis Functions
- Approximation of stochastic partial differential equations by a kernel-based collocation method
- Scattered Data Approximation
This page was built for publication: Kernel-Based Approximation Methods for Partial Differential Equations: Deterministic or Stochastic Problems?