Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Gonnet, Pedro"'
Autor:
Schaller, Matthieu, Borrow, Josh, Draper, Peter W., Ivkovic, Mladen, McAlpine, Stuart, Vandenbroucke, Bert, Bahé, Yannick, Chaikin, Evgenii, Chalk, Aidan B. G., Chan, Tsang Keung, Correa, Camila, van Daalen, Marcel, Elbers, Willem, Gonnet, Pedro, Hausammann, Loïc, Helly, John, Huško, Filip, Kegerreis, Jacob A., Nobels, Folkert S. J., Ploeckinger, Sylvia, Revaz, Yves, Roper, William J., Ruiz-Bonilla, Sergio, Sandnes, Thomas D., Uyttenhove, Yolan, Willis, James S., Xiang, Zhen
Publikováno v:
MNRAS, Volume 530, Issue 2, May 2024, Pages 2378-2419
Numerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical method
Externí odkaz:
http://arxiv.org/abs/2305.13380
Exa-scale simulations are on the horizon but almost no new design for the output has been proposed in recent years. In simulations using individual time steps, the traditional snapshots are over resolving particles/cells with large time steps and are
Externí odkaz:
http://arxiv.org/abs/2210.00835
Autor:
Ferludin, Oleksandr, Eigenwillig, Arno, Blais, Martin, Zelle, Dustin, Pfeifer, Jan, Sanchez-Gonzalez, Alvaro, Li, Wai Lok Sibon, Abu-El-Haija, Sami, Battaglia, Peter, Bulut, Neslihan, Halcrow, Jonathan, de Almeida, Filipe Miguel Gonçalves, Gonnet, Pedro, Jiang, Liangze, Kothari, Parth, Lattanzi, Silvio, Linhares, André, Mayer, Brandon, Mirrokni, Vahab, Palowitch, John, Paradkar, Mihir, She, Jennifer, Tsitsulin, Anton, Villela, Kevin, Wang, Lisa, Wong, David, Perozzi, Bryan
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. In addition to enabling mach
Externí odkaz:
http://arxiv.org/abs/2207.03522
Publikováno v:
Advances in Parallel Computing, Volume 36: Parallel Computing: Technology Trends (2020), Pages: 263 - 274, ISBN: 978-1-64368-070-5
The Friends-of-Friends (FoF) algorithm is a standard technique used in cosmological $N$-body simulations to identify structures. Its goal is to find clusters of particles (called groups) that are separated by at most a cut-off radius. $N$-body simula
Externí odkaz:
http://arxiv.org/abs/2003.11468
Autor:
Gonnet, Pedro, Deselaers, Thomas
We introduce Independently Recurrent Long Short-term Memory cells: IndyLSTMs. These differ from regular LSTM cells in that the recurrent weights are not modeled as a full matrix, but as a diagonal matrix, i.e.\ the output and state of each LSTM cell
Externí odkaz:
http://arxiv.org/abs/1903.08023
Autor:
Carbune, Victor, Gonnet, Pedro, Deselaers, Thomas, Rowley, Henry A., Daryin, Alexander, Calvo, Marcos, Wang, Li-Lun, Keysers, Daniel, Feuz, Sandro, Gervais, Philippe
We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous Segment-and-Decode-based system and reduced the error rate by 20%-40% relati
Externí odkaz:
http://arxiv.org/abs/1902.10525
Publikováno v:
Proceedings of the 13th SPHERIC International Workshop, Galway, Ireland, June 26-28 2018, pp. 44-51
Cosmological simulations require the use of a multiple time-stepping scheme. Without such a scheme, cosmological simulations would be impossible due to their high level of dynamic range; over eleven orders of magnitude in density. Such a large dynami
Externí odkaz:
http://arxiv.org/abs/1807.01341
Publikováno v:
Advances in Parallel Computing, Volume 32: Parallel Computing is Everywhere (2018)
In particle-based simulations, neighbour finding (i.e finding pairs of particles to interact within a given range) is the most time consuming part of the computation. One of the best such algorithms, which can be used for both Molecular Dynamics (MD)
Externí odkaz:
http://arxiv.org/abs/1804.06231
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
We present a new open-source cosmological code, called SWIFT, designed to solve the equations of hydrodynamics using a particle-based approach (Smooth Particle Hydrodynamics) on hybrid shared/distributed-memory architectures. SWIFT was designed from
Externí odkaz:
http://arxiv.org/abs/1606.02738