Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Kovacs, Alexander"'
Autor:
Schaffer, Sebastian, Schrefl, Thomas, Oezelt, Harald, Kovacs, Alexander, Breth, Leoni, Mauser, Norbert J., Suess, Dieter, Exl, Lukas
We study the full 3d static micromagnetic equations via a physics-informed neural network (PINN) ansatz for the continuous magnetization configuration. PINNs are inherently mesh-free and unsupervised learning models. In our approach we can learn to m
Externí odkaz:
http://arxiv.org/abs/2301.13508
Autor:
Breth, Leoni, Fischbacher, Johann, Kovacs, Alexander, Özelt, Harald, Schrefl, Thomas, Brückl, Hubert, Czettl, Christoph, Kührer, Saskia, Pachlhofer, Julia, Schwarz, Maria
First Order Reversal Curve (FORC) diagrams are a popular tool in geophysics and materials science for the characterization of magnetic particles of natural and synthetic origin. However, there is still a lot of controversy about the rigorous interpre
Externí odkaz:
http://arxiv.org/abs/2207.11011
Autor:
Kornell, Alexander, Exl, Lukas, Breth, Leoni, Fischbacher, Johann, Kovacs, Alexander, Oezelt, Harald, Gusenbauer, Markus, Yano, Masao, Sakuma, Noritsugu, Kinoshita, Akihito, Shoji, Tetsuya, Kato, Akira, Mauser, Norbert J., Schrefl, Thomas
This work introduces a latent space method to calculate the demagnetization reversal process of multigrain permanent magnets. The algorithm consists of two deep learning models based on neural networks. The embedded Stoner-Wohlfarth method is used as
Externí odkaz:
http://arxiv.org/abs/2205.03708
Autor:
Kovacs, Alexander, Exl, Lukas, Kornell, Alexander, Fischbacher, Johann, Hovorka, Markus, Gusenbauer, Markus, Breth, Leoni, Oezelt, Harald, Yano, Masao, Sakuma, Noritsugu, Kinoshita, Akihito, Shoji, Tetsuya, Kato, Akira, Schrefl, Thomas
We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-driven approach, we learn the demagnetization curve from data-sets crea
Externí odkaz:
http://arxiv.org/abs/2203.16676
Autor:
Gusenbauer, Markus, Stanciu, Stefan, Kovacs, Alexander, Oezelt, Harald, Fischbacher, Johann, Zhao, Panpan, Woodcock, Thomas George, Schrefl, Thomas
Publikováno v:
In Journal of Magnetism and Magnetic Materials 15 September 2024 606
Autor:
Oezelt, Harald, Qu, Luman, Kovacs, Alexander, Fischbacher, Johann, Gusenbauer, Markus, Beigelbeck, Roman, Praetorius, Dirk, Yano, Masao, Shoji, Tetsuya, Kato, Akira, Chantrell, Roy, Winklhofer, Michael, Zimanyi, Gergely, Schrefl, Thomas
In this paper, we address the problem that standard stochastic Landau-Lifshitz-Gilbert (sLLG) simulations typically produce results that show unphysical mesh-size dependence. The root cause of this problem is that the effects of spin wave fluctuation
Externí odkaz:
http://arxiv.org/abs/2108.10582
Autor:
Kovacs, Alexander, Exl, Lukas, Kornell, Alexander, Fischbacher, Johann, Hovorka, Markus, Gusenbauer, Markus, Breth, Leoni, Oezelt, Harald, Praetorius, Dirk, Suess, Dieter, Schrefl, Thomas
Partial differential equations and variational problems can be solved with physics informed neural networks (PINNs). The unknown field is approximated with neural networks. Minimizing the residuals of the static Maxwell equation at collocation points
Externí odkaz:
http://arxiv.org/abs/2106.03362
Autor:
Kovacs, Alexander, Exl, Lukas, Kornell, Alexander, Fischbacher, Johann, Hovorka, Markus, Gusenbauer, Markus, Breth, Leoni, Oezelt, Harald, Yano, Masao, Sakuma, Noritsugu, Kinoshita, Akihito, Shoji, Tetsuya, Kato, Akira, Schrefl, Thomas
Publikováno v:
In Journal of Magnetism and Magnetic Materials 15 April 2024 596
Autor:
Ali, Qais, Fischbacher, Johann, Kovacs, Alexander, Oezelt, Harald, Gusenbauer, Markus, Moustafa, Heisam, Böhm, David, Breth, Leoni, Schrefl, Thomas
Publikováno v:
In Physica B: Condensed Matter 1 April 2024 678
Autor:
Szórádová, Andrea, Hojsík, Dalibor, Zdarílek, Martin, Valent, Denis, Nižnanský, Ľuboš, Kovács, Alexander, Hokša, Richard, Šidlo, Jozef
Publikováno v:
In Legal Medicine March 2024 67