Zobrazeno 1 - 10
of 36 432
pro vyhledávání: '"Maurizio, M"'
Autor:
Chang, Yue, Benchekroun, Otman, Chiaramonte, Maurizio M., Chen, Peter Yichen, Grinspun, Eitan
The eigenfunctions of the Laplace operator are essential in mathematical physics, engineering, and geometry processing. Typically, these are computed by discretizing the domain and performing eigendecomposition, tying the results to a specific mesh.
Externí odkaz:
http://arxiv.org/abs/2408.10099
Autor:
Zong, Zeshun, Li, Xuan, Li, Minchen, Chiaramonte, Maurizio M., Matusik, Wojciech, Grinspun, Eitan, Carlberg, Kevin, Jiang, Chenfanfu, Chen, Peter Yichen
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material Point Method (MPM) faithfully simulate large-deformation elastoplasti
Externí odkaz:
http://arxiv.org/abs/2310.17790
Autor:
Chang, Yue, Chen, Peter Yichen, Wang, Zhecheng, Chiaramonte, Maurizio M., Carlberg, Kevin, Grinspun, Eitan
Linear reduced-order modeling (ROM) simplifies complex simulations by approximating the behavior of a system using a simplified kinematic representation. Typically, ROM is trained on input simulations created with a specific spatial discretization, a
Externí odkaz:
http://arxiv.org/abs/2310.15907
Autor:
Busso, Maurizio M., Palmerini, Sara
Publikováno v:
Eur. Phys. J. A (2023) 59: 68
We outline a partial historical summary of the steps through which the nucleosynthesis phenomena induced by {\it slow} neutron captures (the {\it s-process}) were clarified, a scientific achievement in which Franz K\"appeler played a major role. We s
Externí odkaz:
http://arxiv.org/abs/2305.01549
Autor:
Chen, Peter Yichen, Xiang, Jinxu, Cho, Dong Heon, Chang, Yue, Pershing, G A, Maia, Henrique Teles, Chiaramonte, Maurizio M., Carlberg, Kevin, Grinspun, Eitan
The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas prior ROM approaches reduce the dimen
Externí odkaz:
http://arxiv.org/abs/2206.02607
Autor:
Maestre, Ana, Martín del Pozo, Mar, Moustafa, Farès, Chopard, Romain, Nieto, José Antonio, Fidalgo Fernández, María Ángeles, López Miguel, Patricia, Verhamme, Peter, Ciammaichella, Maurizio M., Monreal, Manuel
Publikováno v:
In Thrombosis Research November 2024 243
Publikováno v:
In Computer Networks July 2024 249
This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the $\textit{kinematics}$ by approximating the deformation map using an implicit neural representation that restricts defor
Externí odkaz:
http://arxiv.org/abs/2109.12390
Autor:
Lapébie, François-Xavier, Bura-Rivière, Alessandra, Espitia, Olivier, Bongard, Vanina, Ciammaichella, Maurizio M., Martínez, José González, Sigüenza, Patricia, Giménez, Joaquín Castro, Bertoletti, Laurent, Monreal, Manuel
Publikováno v:
In Journal of Thrombosis and Haemostasis August 2023 21(8):2189-2201
Publikováno v:
In Journal of Computational Physics 1 April 2023 478