Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Kasim, Muhammad Firmansyah"'
Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature. For many years this bottleneck has persisted, as many thought sequential m
Externí odkaz:
http://arxiv.org/abs/2309.12252
The beauty of physics is that there is usually a conserved quantity in an always-changing system, known as the constant of motion. Finding the constant of motion is important in understanding the dynamics of the system, but typically requires mathema
Externí odkaz:
http://arxiv.org/abs/2208.10387
Conservation of energy is at the core of many physical phenomena and dynamical systems. There have been a significant number of works in the past few years aimed at predicting the trajectory of motion of dynamical systems using neural networks while
Externí odkaz:
http://arxiv.org/abs/2208.02632
Deep-learning-based models are increasingly used to emulate scientific simulations to accelerate scientific research. However, accurate, supervised deep learning models require huge amount of labelled data, and that often becomes the bottleneck in em
Externí odkaz:
http://arxiv.org/abs/2111.08498
Autor:
Kasim, Muhammad Firmansyah
This paper presents the forward and backward derivatives of partial eigendecomposition, i.e. where it only obtains some of the eigenpairs, of a real symmetric matrix for degenerate cases. The numerical calculation of forward and backward derivatives
Externí odkaz:
http://arxiv.org/abs/2011.04366
The customizable nature of deep learning models have allowed them to be successful predictors in various disciplines. These models are often trained with respect to thousands or millions of instances for complicated problems, but the gathering of suc
Externí odkaz:
http://arxiv.org/abs/1912.10559
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 novel design of 3D spectrometer that can retrieve 3D spectral profile in a single measurement. The 3D spectrometer design is built upon the concept of compressed sensing to make it possible to retrieve 3D information from 2D data from a
Externí odkaz:
http://arxiv.org/abs/1802.00504
Autor:
Chen, Nicholas F. Y., Kasim, Muhammad Firmansyah, Ceurvorst, Luke, Ratan, Naren, Sadler, James, Levy, Matthew C., Trines, Raoul, Bingham, Robert, Norreys, Peter
Publikováno v:
Phys. Rev. E 95, 043305 (2017)
Proton radiography is a technique extensively used to resolve magnetic field structures in high energy density plasmas, revealing a whole variety of interesting phenomena such as magnetic reconnection and collisionless shocks found in astrophysical s
Externí odkaz:
http://arxiv.org/abs/1608.05582
Autor:
Kasim, Muhammad Firmansyah, Ceurvorst, Luke, Ratan, Naren, Sadler, James, Chen, Nicholas, Savert, Alexander, Trines, Raoul, Bingham, Robert, Burrows, Philip N., Kaluza, Malte C., Norreys, Peter
Publikováno v:
Phys. Rev. E 95, 023306 (2017)
Shadowgraphy is a technique widely used to diagnose objects or systems in various fields in physics and engineering. In shadowgraphy, an optical beam is deflected by the object and then the intensity modulation is captured on a screen placed some dis
Externí odkaz:
http://arxiv.org/abs/1607.04179