Stable soft extrapolation of entire functions
Soft extrapolation refers to the problem of recovering a function from its samples, multiplied by a fast-decaying window and perturbed by an additive noise, over an interval which is potentially larger than the essential support of the window. To achieve stable recovery one must use some prior knowl...
Main Authors: | Batenkov, Dmitry (Author), Demanet, Laurent (Author), Mhaskar, Hrushikesh N (Author) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Mathematics (Contributor) |
Format: | Article |
Language: | English |
Published: |
IOP Publishing,
2019-11-14T20:02:56Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Low-frequency extrapolation with deep learning
by: Sun, Hongyu, et al.
Published: (2020) -
Extrapolated full-waveform inversion: An image-space approach
by: Li, Yunyue, et al.
Published: (2018) -
Full-waveform inversion with extrapolated low-frequency data
by: Li, Yunyue, et al.
Published: (2017) -
Extrapolated full-waveform inversion (EFWI) with synthesized low-frequency data
by: Li, Yunyue, et al.
Published: (2018) -
Kernel-Based Analysis of Massive Data
by: Hrushikesh N. Mhaskar
Published: (2020-10-01)