| KEYWORD |
Uncertainty estimation for kernel-based interpolation/extrapolation algorithms in the context of inverse problems: Applications to astronomical imaging.
keywords KERNEL INTERPOLATION, UNCERTAINTY ESTIMATION
Reference persons EMMA PERRACCHIONE
Thesis type THEORETICAL AND NUMERICAL
Description We investigate an interpolation/extrapolation method that, given scattered observations of the Fourier transform, approximates its inverse. The interpolation algorithm takes advantage of modeling the available data via a shape-driven interpolation based on variably scaled Kernels (VSKs), whose implementation is here tailored for inverse problems. The so-constructed interpolants are used as inputs for a standard iterative inversion scheme. The main goal of the thesis is to investigate how the uncertainty propagates during the inversion process.
Deadline 27/01/2024
PROPONI LA TUA CANDIDATURA