Zobrazeno 1 - 10
of 1 484
pro vyhledávání: '"Alghamdi, A. M."'
Autor:
Seelinger, Linus, Reinarz, Anne, Lykkegaard, Mikkel B., Akers, Robert, Alghamdi, Amal M. A., Aristoff, David, Bangerth, Wolfgang, Bénézech, Jean, Diez, Matteo, Frey, Kurt, Jakeman, John D., Jørgensen, Jakob S., Kim, Ki-Tae, Kent, Benjamin M., Martinelli, Massimiliano, Parno, Matthew, Pellegrini, Riccardo, Petra, Noemi, Riis, Nicolai A. B., Rosenfeld, Katherine, Serani, Andrea, Tamellini, Lorenzo, Villa, Umberto, Dodwell, Tim J., Scheichl, Robert
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-l
Externí odkaz:
http://arxiv.org/abs/2402.13768
Autor:
Alghamdi, Amal M A, Riis, Nicolai A B, Afkham, Babak M, Uribe, Felipe, Christensen, Silja L, Hansen, Per Christian, Jørgensen, Jakob S
Inverse problems, particularly those governed by Partial Differential Equations (PDEs), are prevalent in various scientific and engineering applications, and uncertainty quantification (UQ) of solutions to these problems is essential for informed dec
Externí odkaz:
http://arxiv.org/abs/2305.16951
Autor:
Riis, Nicolai A B, Alghamdi, Amal M A, Uribe, Felipe, Christensen, Silja L, Afkham, Babak M, Hansen, Per Christian, Jørgensen, Jakob S
This paper introduces CUQIpy, a versatile open-source Python package for computational uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series. CUQIpy employs a Bayesian framework, integrating prior knowledge wit
Externí odkaz:
http://arxiv.org/abs/2305.16949
Autor:
Alghamdi, Majed M.1,2 (AUTHOR) mmalghamdi@kku.edu.sa, Russell, Gregory T.2 (AUTHOR) greg.russell@canterbury.ac.nz
Publikováno v:
Polymers (20734360). Nov2024, Vol. 16 Issue 22, p3225. 24p.
Autor:
Alghamdi, Jawaher M.1 (AUTHOR) jaalghamdi@ksu.edu.sa, Al-Qahtani, Arwa A.2 (AUTHOR) arahalqahtani@imamu.edu.sa, Alhamlan, Fatimah S.3,4 (AUTHOR) falhamlan@kfshrc.edu.sa, Al-Qahtani, Ahmed A.3,4 (AUTHOR) aqahtani@kfshrc.edu.sa
Publikováno v:
Pharmaceutics. Nov2024, Vol. 16 Issue 11, p1416. 23p.
Publikováno v:
Communications in Mathematics, Volume 31 (2023), Issue 1 (February 14, 2023) cm:10267
In this paper, we study regularity of weak solutions to the incompressible Navier-Stokes equations in $\mathbb{R}^{3}\times (0,T)$. The main goal is to establish the regularity criterion via the gradient of one velocity component in multiplier spaces
Externí odkaz:
http://arxiv.org/abs/2211.02554
Autor:
Bilal, Mohsin, Jewsbury, Robert, Wang, Ruoyu, AlGhamdi, Hammam M., Asif, Amina, Eastwood, Mark, Rajpoot, Nasir
Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this
Externí odkaz:
http://arxiv.org/abs/2211.01256
We propose a novel method for generating high-resolution videos of talking-heads from speech audio and a single 'identity' image. Our method is based on a convolutional neural network model that incorporates a pre-trained StyleGAN generator. We model
Externí odkaz:
http://arxiv.org/abs/2209.04252
In this paper we introduce an algorithm based on a sparse grid adaptive refinement, for the approximation of the eigensolutions to parametric problems arising from elliptic partial differential equations. In particular, we are interested in detecting
Externí odkaz:
http://arxiv.org/abs/2208.14054
Autor:
Alghamdi, Azzah M.
Publikováno v:
In International Journal of Biological Macromolecules January 2025 287