Rational kernel-based interpolation for complex-valued frequency response functions
DOI10.1137/23M1588901MaRDI QIDQ6649892
Ulrich Römer, Julien Bect, Sebastian Schöps, Niklas Georg
Publication date: 6 December 2024
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
dynamical systemsmodel selectionrational approximationfrequency response functioncomplex-valued kernel methods
Gaussian processes (60G15) Approximation by rational functions (41A20) Numerical interpolation (65D05) Numerical methods for initial value problems involving ordinary differential equations (65L05) Laplace transform (44A10) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22) Algorithms for approximation of functions (65D15) Numerical methods for partial differential equations, boundary value problems (65N99)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Transfer function and transient estimation by Gaussian process regression in the frequency domain
- Identification and rational \(L^ 2\) approximation: A gradient algorithm
- A new kernel-based approach for linear system identification
- The PAC-MAN model: benchmark case for linear acoustics in computational physics
- A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices
- FRF estimation using multiple kernel-based regularisation
- Least-squares Padé approximation of parametric and stochastic Helmholtz maps
- The quadratic eigenvalue problem
- Function theory of several complex variables
- An introduction to the theory of reproducing kernel Hilbert spaces
- Widely Linear Complex-Valued Kernel Methods for Regression
- Stability Selection
- Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates
- Complex Gaussian Processes
- On Learning Vector-Valued Functions
- The AAA Algorithm for Rational Approximation
- Sparse Bayesian learning for complex‐valued rational approximations
- The p-AAA Algorithm for Data-Driven Modeling of Parametric Dynamical Systems
- Toward a certified greedy Loewner framework with minimal sampling
- Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria
- An adaptive sparse grid rational Arnoldi method for uncertainty quantification of dynamical systems in the frequency domain
This page was built for publication: Rational kernel-based interpolation for complex-valued frequency response functions
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6649892)
