Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Babak Maboudi Afkham"'
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 11:31-61
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
Publikováno v:
Afkham, B M, Chung, J & Chung, M 2021, ' Learning regularization parameters of inverse problems via deep neural networks : Paper ', Inverse Problems, vol. 37, no. 10, 105017 . https://doi.org/10.1088/1361-6420/ac245d
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::745a107e397085fb925440daa2e55f84
https://orbit.dtu.dk/en/publications/c7e8da06-2eb7-4051-a13d-fbf7c45f8619
https://orbit.dtu.dk/en/publications/c7e8da06-2eb7-4051-a13d-fbf7c45f8619
Publikováno v:
Lecture Notes in Computational Science and Engineering ISBN: 9783030487201
In the past decade, model order reduction (MOR) has been successful in reducing the computational complexity of elliptic and parabolic systems of partial differential equations (PDEs). However, MOR of hyperbolic equations remains a challenge. Symmetr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::594f7461a465da118c17d371c5c9568a
https://doi.org/10.1007/978-3-030-48721-8_4
https://doi.org/10.1007/978-3-030-48721-8_4
Autor:
Jan S. Hesthaven, Babak Maboudi Afkham
While reduced-order models (ROMs) are popular for approximately solving large systems of differential equations, the stability of reduced models over long-time integration remains an open question. We present a greedy approach for ROM generation of p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9809e60a7a538ad723626fc1ae3a2aea
Autor:
Jan S. Hesthaven, Babak Maboudi Afkham
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cff5ba85e49631c5e6080543371b8c4
https://infoscience.epfl.ch/record/227381
https://infoscience.epfl.ch/record/227381