Zobrazeno 1 - 10
of 149
pro vyhledávání: '"Shoji, Tetsuya"'
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:
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, 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:
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 introduce conditional PINNs (physics informed neural networks) for estimating the solution of classes of eigenvalue problems. The concept of PINNs is expanded to learn not only the solution of one particular differential equation but the solutions
Externí odkaz:
http://arxiv.org/abs/2104.02741
Autor:
Harashima, Yosuke, Tamai, Keiichi, Doi, Shotaro, Matsumoto, Munehisa, Akai, Hisazumi, Kawashima, Naoki, Ito, Masaaki, Sakuma, Noritsugu, Kato, Akira, Shoji, Tetsuya, Miyake, Takashi
Publikováno v:
Phys. Rev. Materials 5, 013806 (2021)
We propose a data-assimilation method for evaluating the finite-temperature magnetization of a permanent magnet over a high-dimensional composition space. Based on a general framework for constructing a predictor from two data sets including missing
Externí odkaz:
http://arxiv.org/abs/2007.14101
Autor:
Matsumoto, Munehisa, Ito, Masaaki, Sakuma, Noritsugu, Yano, Masao, Shoji, Tetsuya, Akai, Hisazumi
Prospects for light-rare-earth-based permanent magnet compound R$_{2}$Fe$_{14}$B (R=La$_{1-x}$Ce$_{x}$ with $0 \le x\le 1$) are inspected from first principles referring to the latest experimental data. Ce-rich 2:14:1 compounds come with good structu
Externí odkaz:
http://arxiv.org/abs/1901.10119
Autor:
Exl, Lukas, Fischbacher, Johann, Kovacs, Alexander, Oezelt, Harald, Gusenbauer, Markus, Yokota, Kazuya, Shoji, Tetsuya, Hrkac, Gino, Schrefl, Thomas
Publikováno v:
J. Phys. Mater. 2 (2019) 014001
We use a machine learning approach to identify the importance of microstructure characteristics in causing magnetization reversal in ideally structured large-grained Nd$_2$Fe$_{14}$B permanent magnets. The embedded Stoner-Wohlfarth method is used as
Externí odkaz:
http://arxiv.org/abs/1808.03794
Autor:
Skelland, Connor, Ostler, Thomas, Westland, Samuel, Evans, Richard, Chantrell, Roy, Yano, Masao, Shoji, Tetsuya, Manabe, Akira, Kato, Akira, Winklhofer, Michael, Zimanyi, Gergely, Fischbacher, Johannes, Schrefl, Thomas, Hrkac, Gino
We investigated the atomic fill site probability distributions across supercell structures of RT12-xTi (R=Nd, Sm, T=Fe, Co). We use a combined molecular dynamics and Boltzmann distribution approach to extrapolate the probability distributions for Ti
Externí odkaz:
http://arxiv.org/abs/1804.05608
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 Communications in Nonlinear Science and Numerical Simulation January 2022 104