73 citations · 132 across the 4 of their papers we have counts for
4 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…
Magnetostatics and micromagnetics with physics informed neural networks
Alexander Kovacs, Lukas Exl, Alexander Kornell +8
Partial differential equations and variational problems can be solved with physics informed neural networks (PINNs). The unknown field is approximated with neural networks. Minimiz…
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…