Zobrazeno 1 - 10
of 235
pro vyhledávání: '"GELB, ANNE"'
We propose an entropy-stable conservative flux form neural network (CFN) that integrates classical numerical conservation laws into a data-driven framework using the entropy-stable, second-order, and non-oscillatory Kurganov-Tadmor (KT) scheme. The p
Externí odkaz:
http://arxiv.org/abs/2411.01746
Autor:
Li, Tongtong, Gelb, Anne
Fourier partial sum approximations yield exponential accuracy for smooth and periodic functions, but produce the infamous Gibbs phenomenon for non-periodic ones. Spectral reprojection resolves the Gibbs phenomenon by projecting the Fourier partial su
Externí odkaz:
http://arxiv.org/abs/2406.15977
Recovering complex-valued image recovery from noisy indirect data is important in applications such as ultrasound imaging and synthetic aperture radar. While there are many effective algorithms to recover point estimates of the magnitude, fewer are d
Externí odkaz:
http://arxiv.org/abs/2403.16992
Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models typically comprise a conditionally Gaussian prior model for the unknown which is augmented by a generalized gamma hyp
Externí odkaz:
http://arxiv.org/abs/2402.16623
We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps. The complex nature of image data and the lack of temporal information pose significant challenges in capturing the unique evolving
Externí odkaz:
http://arxiv.org/abs/2310.09495
Ensemble transform Kalman filtering (ETKF) data assimilation is often used to combine available observations with numerical simulations to obtain statistically accurate and reliable state representations in dynamical systems. However, it is well know
Externí odkaz:
http://arxiv.org/abs/2309.02585
Autor:
Glaubitz, Jan, Gelb, Anne
Publikováno v:
SIAM-ASA J Uncertain Quantif 12(2), 2024
We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors. Our model uses separate conditionally Gaussian priors for each parameter vector and common gamma-distributed hyper-param
Externí odkaz:
http://arxiv.org/abs/2303.16954
This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new method is cast in a Bayesian framework and uses a prior that simultaneously
Externí odkaz:
http://arxiv.org/abs/2302.14247
We propose a new data-driven method to learn the dynamics of an unknown hyperbolic system of conservation laws using deep neural networks. Inspired by classical methods in numerical conservation laws, we develop a new conservative form network (CFN)
Externí odkaz:
http://arxiv.org/abs/2211.14375
Autor:
Churchill, Victor, Gelb, Anne
In the problem of spotlight mode airborne synthetic aperture radar (SAR) image formation, it is well-known that data collected over a wide azimuthal angle violate the isotropic scattering property typically assumed. Many techniques have been proposed
Externí odkaz:
http://arxiv.org/abs/2208.12292