Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Afkham, Babak Maboudi"'
This paper considers a Bayesian approach for inclusion detection in nonlinear inverse problems using two known and popular push-forward prior distributions: the star-shaped and level set prior distributions. We analyze the convergence of the correspo
Externí odkaz:
http://arxiv.org/abs/2308.13673
This work describes a Bayesian framework for reconstructing the boundaries that represent targeted features in an image, as well as the regularity (i.e., roughness vs. smoothness) of these boundaries.This regularity often carries crucial information
Externí odkaz:
http://arxiv.org/abs/2305.04608
In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient goal-oriented uncertainty quantification for inverse problems. Contrary to standard inverse problems, these approaches are \emph{goal-oriented
Externí odkaz:
http://arxiv.org/abs/2304.08324
In this work, we describe a Bayesian framework for reconstructing the boundaries of piecewise smooth regions in the X-ray computed tomography (CT) problem in an infinite-dimensional setting. In addition to the reconstruction, we are also able to quan
Externí odkaz:
http://arxiv.org/abs/2107.06607
In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning approach, where a network is trained to approximate the mapping from obs
Externí odkaz:
http://arxiv.org/abs/2104.06594
In the recent years, considerable attention has been paid to preserving structures and invariants in reduced basis methods, in order to enhance the stability and robustness of the reduced system. In the context of Hamiltonian systems, symplectic mode
Externí odkaz:
http://arxiv.org/abs/1803.07799
Reduced basis methods are popular for approximately solving large and complex systems of differential equations. However, conventional reduced basis methods do not generally preserve conservation laws and symmetries of the full order model. Here, we
Externí odkaz:
http://arxiv.org/abs/1705.00498
While reduced-order models (ROMs) have been popular for efficiently solving large systems of differential equations, the stability of reduced models over long-time integration is of present challenges. We present a greedy approach for ROM generation
Externí odkaz:
http://arxiv.org/abs/1703.08345
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This work describes a Bayesian framework for reconstructing functions that represents the targeted features with uncertain regularity, i.e., roughness vs. smoothness. The regularity of functions carries crucial information in many inverse problem app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6429a9749623fcae3b42a33acb58fe57