Zobrazeno 1 - 10
of 7 024
pro vyhledávání: '"Frutos, P"'
Traditional implicit generative models are capable of learning highly complex data distributions. However, their training involves distinguishing real data from synthetically generated data using adversarial discriminators, which can lead to unstable
Externí odkaz:
http://arxiv.org/abs/2410.22381
Autor:
Galano-Frutos, Juan José, Bergamasco, Luca, Vigo, Paolo, Morciano, Matteo, Fasano, Matteo, Pirolli, Davide, Chiavazzo, Eliodoro, de Rosa, Maria Cristina
Aquaporins play a key role for the regulation of water transport and solute selectivity in biological cells and tissues. Due to their unique properties, during the last years aquaporins (AQPs) have attracted increasing interest for their use in the d
Externí odkaz:
http://arxiv.org/abs/2410.14355
Autor:
Griesemer, Tina, Ximenes, Rui Franqueira, Ahdida, Claudia, Izquierdo, Gonzalo Arnau, Santillana, Ignacio Aviles, Callaghan, Jack, Dumont, Gerald, Dutilleul, Thomas, Terricabras, Adria Gallifa, Höll, Stefan, Jacobsson, Richard, Kyffin, William, Mamun, Abdullah Al, Mazzola, Giuseppe, Fontenla, Ana Teresa Pérez, De Frutos, Oscar Sacristan, Esposito, Luigi Salvatore, Sgobba, Stefano, Calviani, Marco
Particle-producing targets in high-energy research facilities are often made from refractory metals, and they typically require dedicated cooling systems due to the challenging thermomechanical conditions they experience. However, direct contact of w
Externí odkaz:
http://arxiv.org/abs/2410.01988
Autor:
Griesemer, Tina, Ximenes, Rui Franqueira, Izquierdo, Gonzalo Arnau, Santillana, Ignacio Aviles, Brehm, Thomas, Terricabras, Adria Gallifa, Höll, Stefan, Jacobsson, Richard, Kaiser, Marco, Kuchar, Roman, Fontenla, Ana Teresa Pérez, Rempel, Alexey, De Frutos, Oscar Sacristan, Schienbein, Marcel, Sgobba, Stefano, Calviani, Marco
The Beam Dump Facility (BDF) is a planned fixed-target installation in CERN's North Area, set to start operating in 2031. A proton beam of 400 GeV/c will be delivered in 1 s pulses of 4e13 protons every 7.2 s, amounting to 4e19 protons on target (PoT
Externí odkaz:
http://arxiv.org/abs/2410.01964
Autor:
Huergo, David, de Frutos, Martín, Jané, Eduardo, Marino, Oscar A., Rubio, Gonzalo, Ferrer, Esteban
We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p solvers using Reinforcement Learning (RL). The dynamic RL adaptation uses the evolving solution to adjust the high-order polynomials. We develop an offlin
Externí odkaz:
http://arxiv.org/abs/2407.19000
We develop a torque-pitch control framework using deep reinforcement learning for wind turbines to optimize the generation of wind turbine energy while minimizing operational noise. We employ a double deep Q-learning, coupled to a blade element momen
Externí odkaz:
http://arxiv.org/abs/2407.13320
In this contribution, the classical tests of general relativity using the Gutsunaev-Manko metric with slow rotation are obtained. This metric represents the spacetime of an object endowed with mass, magnetic dipole moment and angular moment. These te
Externí odkaz:
http://arxiv.org/abs/2405.03503
In this contribution, we present a short account of gravitational lenses and how to calculate different properties of its images in the case of having a transparent distribution of matter such as the uniform transparent sphere, isothermal gas sphere,
Externí odkaz:
http://arxiv.org/abs/2405.00993
An study of the equatorial circular motion of photons and massive particles around a rotating compact body like a neutron star is presented. For this goal, we use an approximate Kerr-like metric with mass quadrupole as perturbation. The effect of thi
Externí odkaz:
http://arxiv.org/abs/2404.19268
Implicit generative models have the capability to learn arbitrary complex data distributions. On the downside, training requires telling apart real data from artificially-generated ones using adversarial discriminators, leading to unstable training a
Externí odkaz:
http://arxiv.org/abs/2402.16435