2 citations · 5 across the 6 of their papers we have counts for
9 papers
Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data
Victor Churchill
We present a computational technique for modeling the evolution of dynamical systems in a reduced basis, with a focus on the challenging problem of modeling partially-observed part…
Deep Learning of Chaotic Systems from Partially-Observed Data
Victor Churchill, Dongbin Xiu
Recently, a general data driven numerical framework has been developed for learning and modeling of unknown dynamical systems using fully- or partially-observed data. The method ut…
Estimation and uncertainty quantification for piecewise smooth signal recovery
Victor Churchill, Anne Gelb
This paper presents a sparse Bayesian learning (SBL) algorithm for linear inverse problems with a high order total variation (HOTV) sparsity prior. For the problem of sparse signal…
Synthetic Aperture Radar Image Formation with Uncertainty Quantification
Victor Churchill, Anne Gelb
Synthetic aperture radar (SAR) is a day or night any-weather imaging modality that is an important tool in remote sensing. Most existing SAR image formation methods result in a max…
Use of convexity in contour detection
Victor Churchill
In this paper, we formulate a simple algorithm that detects contours around a region of interest in an image. After an initial smoothing, the method is based on viewing an image as…
Total Variation Bayesian Learning via Synthesis
Victor Churchill, Anne Gelb
This paper presents a sparse Bayesian learning algorithm for inverse problems in signal and image processing with a total variation (TV) sparsity prior. Because of the prior used,…