38 citations · 39 across the 5 of their papers we have counts for
10 papers
Description of collective magnetization processes with machine learning models
Alexander Kornell, Lukas Exl, Leoni Breth +11
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 bas…
Exploring the hysteresis properties of nanocrystalline permanent magnets using deep learning
Alexander Kovacs, Lukas Exl, Alexander Kornell +11
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-d…
Full-Spin-Wave-Scaled Finite Element Stochastic Micromagnetism: Mesh-Independent FUSSS LLG Simulations of Ferromagnetic Resonance and Reversal
Harald Oezelt, Luman Qu, Alexander Kovacs +11
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 r…
Conditional physics informed neural networks
Alexander Kovacs, Lukas Exl, Alexander Kornell +11
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…
Data Assimilation Method for Experimental and First-Principles Data: Finite-Temperature Magnetization of (Nd,Pr,La,Ce)(Fe,Co,Ni)B
Yosuke Harashima, Keiichi Tamai, Shotaro Doi +8
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 framew…
Optimal uni-axial ferromagnetism in (La,Ce)FeB for permanent magnets
Munehisa Matsumoto, Masaaki Ito, Noritsugu Sakuma +3
Prospects for light-rare-earth-based permanent magnet compound RFeB (R=LaCe with ) are inspected from first principles referring to the lat…