Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Markus Gusenbauer"'
Autor:
Alexander Kovacs, Johann Fischbacher, Harald Oezelt, Alexander Kornell, Qais Ali, Markus Gusenbauer, Masao Yano, Noritsugu Sakuma, Akihito Kinoshita, Tetsuya Shoji, Akira Kato, Yuan Hong, Stéphane Grenier, Thibaut Devillers, Nora M. Dempsey, Tetsuya Fukushima, Hisazumi Akai, Naoki Kawashima, Takashi Miyake, Thomas Schrefl
Publikováno v:
Frontiers in Materials, Vol 9 (2023)
Rare-earth elements like neodymium, terbium and dysprosium are crucial to the performance of permanent magnets used in various green-energy technologies like hybrid or electric cars. To address the supply risk of those elements, we applied machine-le
Externí odkaz:
https://doaj.org/article/ba3de6b1961f495380a13d05ea592dea
Autor:
Harald Oezelt, Luman Qu, Alexander Kovacs, Johann Fischbacher, Markus Gusenbauer, Roman Beigelbeck, Dirk Praetorius, Masao Yano, Tetsuya Shoji, Akira Kato, Roy Chantrell, Michael Winklhofer, Gergely T. Zimanyi, Thomas Schrefl
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-9 (2022)
Abstract 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 fl
Externí odkaz:
https://doaj.org/article/54c7e470daac4317b1b9d7d6ec6be7c3
Autor:
Alexander Kovacs, Johann Fischbacher, Markus Gusenbauer, Harald Oezelt, Heike C. Herper, Olga Yu. Vekilova, Pablo Nieves, Sergiu Arapan, Thomas Schrefl
Publikováno v:
Engineering, Vol 6, Iss 2, Pp 148-153 (2020)
Multiscale simulation is a key research tool in the quest for new permanent magnets. Starting with first principles methods, a sequence of simulation methods can be applied to calculate the maximum possible coercive field and expected energy density
Externí odkaz:
https://doaj.org/article/3e518bedb17e40aca524e65934fc3e93
Autor:
Ivan Cimrak, Katarina Bachrata, Hynek Bachraty, Iveta Jancigova, Renata Tothova, Martin Busik, Martin Slavik, Markus Gusenbauer
Publikováno v:
Communications, Vol 18, Iss 1A, Pp 13-20 (2016)
We present a fully three-dimensional computational model of red blood cells and their flow in a fluid. This model includes all components necessary to capture important physical and biological aspects of the cell flow. It comprises descriptions of el
Externí odkaz:
https://doaj.org/article/28c91afb7789498b838261cf093d6943
Autor:
Andreas Ney, Daniel Primetzhofer, He Sun, David Doppelbauer, Matthias Kehrer, B. Faina, Julia Lumetzberger, Abdalaziz Aljabour, Markus Gusenbauer, Halime Coskun, Philipp Stadler, Verena Ney, Jiri Duchoslav, Heiko Groiss, David Stifter
Publikováno v:
Materials Advances
Currently, energy-efficient electrocatalytic oxygen evolution from water involves the use of noble metal oxides. Here, we show that highly p-conducting zinc cobaltite spinel Zn1.2Co1.8O3.5 offers an enhanced electrocatalytic activity for oxygen evolu
Autor:
Alexander Kovacs, Lukas Exl, Alexander Kornell, Johann Fischbacher, Markus Hovorka, Markus Gusenbauer, Leoni Breth, Harald Oezelt, Masao Yano, Noritsugu Sakuma, Akihito Kinoshita, Tetsuya Shoji, Akira Kato, Thomas Schrefl
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::284c88693612a1cbb1130b845a739b11
http://arxiv.org/abs/2203.16676
http://arxiv.org/abs/2203.16676
Autor:
Alexander Kovacs, Lukas Exl, Alexander Kornell, Johann Fischbacher, Markus Hovorka, Markus Gusenbauer, Leoni Breth, Harald Oezelt, Dirk Praetorius, Dieter Suess, Thomas Schrefl
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::759ad1052bb91af9b87ef81ada31defd
http://arxiv.org/abs/2106.03362
http://arxiv.org/abs/2106.03362
Autor:
Lukas Exl, Thomas Schrefl, Alexander Kovacs, Markus Gusenbauer, Harald Oezelt, Johann Fischbacher
Publikováno v:
Computer Physics Communications. 235:179-186
Fast computation of demagnetization curves is essential for the computational design of soft magnetic sensors or permanent magnet materials. We show that a sparse preconditioner for a nonlinear conjugate gradient energy minimizer can lead to a speed
Autor:
Masao Yano, Johann Fischbacher, Akira Kato, Tetsuya Shoji, Markus Gusenbauer, Noritsugu Sakuma, Lukas Exl, Leoni Breth, Thomas Schrefl, Alexander Kovacs, Alexander Kornell, Harald Oezelt, Akihito Kinoshita, Markus Hovorka
Publikováno v:
Communications in Nonlinear Science and Numerical Simulation. 104:106041
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
Autor:
Thomas George Woodcock, Alexander Kovacs, Markus Gusenbauer, Panpan Zhao, Thomas Schrefl, Harald Oezelt, Johann Fischbacher
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-10 (2020)
Microstructural features play an important role in the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, like twin boundaries, as is well known for MnAl-C. It would be very useful to be able to predict th