9 Kernel methods for surrogate modeling
From MaRDI portal
Publication:3384280
DOI10.1515/9783110498967-009OpenAlexW4206959576MaRDI QIDQ3384280
Bernard Haasdonk, Gabriele Santin
Publication date: 15 December 2021
Published in: System- and Data-Driven Methods and Algorithms (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1907.10556
reproducing kernel Hilbert spacessupport vector regressionsurrogate modelinggreedy approximationregularized kernel interpolation
Learning and adaptive systems in artificial intelligence (68T05) Hilbert and pre-Hilbert spaces: geometry and topology (including spaces with semidefinite inner product) (46C05) Numerical interpolation (65D05) Algorithms for approximation of functions (65D15)
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Cites Work
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- Unnamed Item
- Recent advances on radial basis function collocation methods
- CAD and mesh repair with radial basis functions
- Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems
- Bases for kernel-based spaces
- Theory of reproducing kernels and applications
- Sampling inequalities for infinitely smooth functions, with applications to interpolation and machine learning
- Heuristic strategies for the approximation of stability factors in quadratically nonlinear parametrized PDEs
- A Newton basis for kernel spaces
- Simulation-based classification; a model-order-reduction approach for structural health monitoring
- Error estimates and condition numbers for radial basis function interpolation
- Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree
- Reduced order modeling for nonlinear structural analysis using Gaussian process regression
- Data-driven reduced order modeling for time-dependent problems
- Efficient computation of partition of unity interpolants through a block-based searching technique
- Greedy kernel methods for accelerating implicit integrators for parametric ODEs
- A transport-based multifidelity preconditioner for Markov chain Monte Carlo
- Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario
- Approximate interpolation with applications to selecting smoothing parameters
- Multivariate interpolation for fluid-structure-interaction problems using radial basis functions
- Stable Computation of Differentiation Matrices and Scattered Node Stencils Based on Gaussian Radial Basis Functions
- Interpolation of spatial data – A stochastic or a deterministic problem?
- Stable Evaluation of Gaussian Radial Basis Function Interpolants
- The ROMES Method for Statistical Modeling of Reduced-Order-Model Error
- Surrogate modeling of multiscale models using kernel methods
- A Fluid–Structure Interaction Algorithm Using Radial Basis Function Interpolation Between Non-Conforming Interfaces
- Stable Computations with Gaussian Radial Basis Functions
- A Primer on Radial Basis Functions with Applications to the Geosciences
- Support Vector Machines
- An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels
- Atomic Decomposition by Basis Pursuit
- ON THE INCLUSION RELATION OF REPRODUCING KERNEL HILBERT SPACES
- A Rescaled Localized Radial Basis Function Interpolation on Non-Cartesian and Nonconforming Grids
- Greedy Kernel Approximation for Sparse Surrogate Modeling
- Kernel Methods for the Approximation of Nonlinear Systems
- Convergence rate of the data-independent $P$-greedy algorithm in kernel-based approximation
- A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
- On Learning Vector-Valued Functions
- Scattered Data Approximation