Zobrazeno 1 - 10
of 1 850
pro vyhledávání: '"Schrefl A"'
Autor:
Zenbaa, Noura, Majcen, Fabian, Abert, Claas, Bruckner, Florian, Mauser, Norbert J., Schrefl, Thomas, Wang, Qi, Suess, Dieter, Chumak, Andrii V.
Magnonic logic gates represent a crucial step toward realizing fully magnonic data processing systems without reliance on conventional electronic or photonic elements. Recently, a universal and reconfigurable inverse-design device has been developed,
Externí odkaz:
http://arxiv.org/abs/2411.17546
Autor:
Abert, Claas, Bruckner, Florian, Voronov, Andrey, Lang, Martin, Pathak, Swapneel Amit, Holt, Samuel, Kraft, Robert, Allayarov, Ruslan, Flauger, Peter, Koraltan, Sabri, Schrefl, Thomas, Chumak, Andrii, Fangohr, Hans, Suess, Dieter
We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various paralle
Externí odkaz:
http://arxiv.org/abs/2411.11725
Autor:
Sandoval, Miguel A. Cascales, Jurczyk, J., Skoric, L., Sanz-Hernández, D., Leo, N., Kovacs, A., Schrefl, T., Hierro-Rodríguez, A., Fernández-Pacheco, A.
In-operando techniques enable real-time measurement of intricate physical properties at the micro- and nano-scale under external stimuli, allowing the study of a wide range of materials and functionalities. In nanomagnetism, in-operando techniques gr
Externí odkaz:
http://arxiv.org/abs/2411.10374
In this work, we explore advanced machine learning techniques for minimizing Gibbs free energy in full 3D micromagnetic simulations. Building on Brown's bounds for magnetostatic self-energy, we revisit their application in the context of variational
Externí odkaz:
http://arxiv.org/abs/2409.12877
Autor:
Zenbaa, Noura, Abert, Claas, Majcen, Fabian, Kerber, Michael, Serha, Rostyslav O., Knauer, Sebastian, Wang, Qi, Schrefl, Thomas, Suess, Dieter, Chumak, Andrii V.
In the field of magnonics, which uses magnons, the quanta of spin waves, for energy-efficient data processing, significant progress has been made leveraging the capabilities of the inverse design concept. This approach involves defining a desired fun
Externí odkaz:
http://arxiv.org/abs/2403.17724
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:
Luca, Sorana, Fischbacher, Johann, Flament, Camille, Sedek, Ryan, de Rango, Patricia, Eslava, Gabriel Gomez, Schrefl, Thomas
Publikováno v:
In Journal of Alloys and Compounds 5 January 2025 1010
Publikováno v:
Current Issues in Sport Science, Vol 9, Iss 1 (2024)
The single leg heel rise (SLHR) test is a widely used method for assessing calf muscle-tendon unit (MTU) endurance in various fields, including medicine, sports, and dance. The objectives of this study were to examine the reliability of a standardize
Externí odkaz:
https://doaj.org/article/a13dbfcf7cf544098384b5cac7b98b47
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