148 citations · 1.1k across the 41 of their papers we have counts for
41 papers
An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics
Sebastian Schaffer, Lukas Exl
We develop an energy-based reduced-order model for micromagnetic magnetization dynamics that couples a convolutional autoencoder to a structured latent neural ordinary differential…
Physics-informed low-rank neural operators with application to parametric elliptic PDEs
Sebastian Schaffer, Lukas Exl
We present the Physics-Informed Low-Rank Neural Operator (PILNO), a neural operator framework for efficiently approximating solution operators of partial differential equations (PD…
Near-field-free super-potential FFT method for the three-dimensional free-space Poisson equation
Lukas Exl, Sebastian Schaffer
We present a spectrally accurate, efficient FFT-based method for the three-dimensional free-space Poisson equation with smooth, compactly supported sources. The method adopts a sup…
Higher order stray field computation on tensor product domains
Lukas Exl, Sebastian Schaffer
We present an extension of the tensor grid method for stray field computation on rectangular domains that incorporates higher-order basis functions. Both the magnetization and the…
Physics aware machine learning for micromagnetic energy minimization: recent algorithmic developments
Sebastian Schaffer, Thomas Schrefl, Harald Oezelt +2
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 se…
Physics-informed machine learning and stray field computation with application to micromagnetic energy minimization
Sebastian Schaffer, Thomas Schrefl, Harald Oezelt +5
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-fr…
Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models
Claus O. W. Trost, Stanislav Zak, Sebastian Schaffer +3
As the need for miniaturized structural and functional materials has increased,the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method…
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…
Machine learning methods for the prediction of micromagnetic magnetization dynamics
Sebastian Schaffer, Norbert J. Mauser, Thomas Schrefl +2
Machine learning (ML) entered the field of computational micromagnetics only recently. The main objective of these new approaches is the automatization of solutions of parameter-de…
Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method
Lukas Exl, Norbert J. Mauser, Sebastian Schaffer +2
We establish a machine learning model for the prediction of the magnetization dynamics as function of the external field described by the Landau-Lifschitz-Gilbert equation, the par…
Optimal control of the self-bound dipolar droplet formation process
Jan-Frederik Mennemann, Tim Langen, Lukas Exl +1
Dipolar Bose-Einstein condensates have recently attracted much attention in the world of quantum many body experiments. While the theoretical principles behind these experiments ar…
Learning time-stepping by nonlinear dimensionality reduction to predict magnetization dynamics
Lukas Exl, Norbert J. Mauser, Thomas Schrefl +1
We establish a time-stepping learning algorithm and apply it to predict the solution of the partial differential equation of motion in micromagnetism as a dynamical system dependin…
Micromagnetics of rare-earth efficient permanent magnets
Johann Fischbacher, Alexander Kovacs, Markus Gusenbauer +4
The development of permanent magnets containing less or no rare-earth elements is linked to profound knowledge of the coercivity mechanism. Prerequisites for a promising permanent…
Learning magnetization dynamics
Alexander Kovacs, Johann Fischbacher, Harald Oezelt +5
Deep neural networks are used to model the magnetization dynamics in magnetic thin film elements. The magnetic states of a thin film element can be represented in a low dimensional…
Computational micromagnetics with Commics
Carl-Martin Pfeiler, Michele Ruggeri, Bernhard Stiftner +6
We present our open-source Python module Commics for the study of the magnetization dynamics in ferromagnetic materials via micromagnetic simulations. It implements state-of-the-ar…
Magnetic microstructure machine learning analysis
Lukas Exl, Johann Fischbacher, Alexander Kovacs +6
We use a machine learning approach to identify the importance of microstructure characteristics in causing magnetization reversal in ideally structured large-grained NdFe$_{14}…
Many-body physics in two-component Bose-Einstein condensates in a cavity: fragmented superradiance and polarization
Axel U. J. Lode, Fritz S. Diorico, Rugway Wu +8
We consider laser-pumped one-dimensional two-component bosons in a parabolic trap embedded in a high-finesse optical cavity. Above a threshold pump power, the photons that populate…
Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization
Lukas Exl, Johann Fischbacher, Alexander Kovacs +3
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 fo…
An optimization approach for dynamical Tucker tensor approximation
Lukas Exl
An optimization-based approach for the Tucker tensor approximation of parameter-dependent data tensors and solutions of tensor differential equations with low Tucker rank is presen…
On the limits of coercivity in permanent magnets
J. Fischbacher, A. Kovacs, H. Oezelt +14
The maximum coercivity that can be achieved for a given hard magnetic alloy is estimated by computing the energy barrier for the nucleation of a reversed domain in an idealized mic…
The extrapolated explicit midpoint scheme for variable order and step size controlled integration of the Landau-Lifschitz-Gilbert equation
Lukas Exl, Norbert J. Mauser, Thomas Schrefl +1
A practical and efficient scheme for the higher order integration of the Landau-Lifschitz-Gilbert (LLG) equation is presented. The method is based on extrapolation of the two-step…