Zobrazeno 1 - 10
of 493
pro vyhledávání: '"P. Ryckelynck"'
Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is cruc
Externí odkaz:
http://arxiv.org/abs/2402.17372
In the context of a group project for the course COMSW4995 002 - Geometric Data Analysis, we bring our attention to the design of fast-typing keyboards. Leveraging some geometric tools in an optimization framework allowed us to propose novel keyboard
Externí odkaz:
http://arxiv.org/abs/2310.10956
Publikováno v:
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2023, pp. 4130-4140
When it comes to clinical images, automatic segmentation has a wide variety of applications and a considerable diversity of input domains, such as different types of Magnetic Resonance Images (MRIs) and Computerized Tomography (CT) scans. This hetero
Externí odkaz:
http://arxiv.org/abs/2310.05572
Convolutional neural networks are now seeing widespread use in a variety of fields, including image classification, facial and object recognition, medical imaging analysis, and many more. In addition, there are applications such as physics-informed s
Externí odkaz:
http://arxiv.org/abs/2304.04964
Publikováno v:
IFAC PAPERSONLINE, 55(20), 469-474, (2022)
The analysis of parametric and non-parametric uncertainties of very large dynamical systems requires the construction of a stochastic model of said system. Linear approaches relying on random matrix theory and principal componant analysis can be used
Externí odkaz:
http://arxiv.org/abs/2110.13680
Model order reduction has been extensively studied over the last two decades. Projection-based methods such as the Proper Orthogonal Decomposition and the Reduced Basis Method enjoy the important advantages of Galerkin methods in the derivation of th
Externí odkaz:
http://arxiv.org/abs/2108.12291
Publikováno v:
Mech. Ind., 23, (2022)
We consider the dictionary-based ROM-net (Reduced Order Model) framework [T. Daniel, F. Casenave, N. Akkari, D. Ryckelynck, Model order reduction assisted by deep neural networks (ROM-net), Advanced modeling and Simulation in Engineering Sciences 7 (
Externí odkaz:
http://arxiv.org/abs/2108.04012
Publikováno v:
Front. Mater., 25 November 2021 Sec. Computational Materials Science
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome, demanding automated data pipelines capable of dealing with non-trivia
Externí odkaz:
http://arxiv.org/abs/2107.07468
Publikováno v:
J. Comput. Phys., 458, 111120 (2022)
Nonlinear model order reduction has opened the door to parameter optimization and uncertainty quantification in complex physics problems governed by nonlinear equations. In particular, the computational cost of solving these equations can be reduced
Externí odkaz:
http://arxiv.org/abs/2103.13683
Publikováno v:
Math. Comput. Appl. 26(1), 17, (2021)
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system. For such classification tasks, labeled training data
Externí odkaz:
http://arxiv.org/abs/2101.04530