73 citations · 89 across the 3 of their papers we have counts for
6 papers
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…
FORC Diagram Features of Co Particles due to Reversal by Domain Nucleation
Leoni Breth, Johann Fischbacher, Alexander Kovacs +7
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. Ho…
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…
Extracting local switching fields in permanent magnets using machine learning
Markus Gusenbauer, Harald Oezelt, Johann Fischbacher +4
Microstructural features play an important role for the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, which is well known for MnAl…
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…
Micromagnetic Simulations for Coercivity Improvement through Nano-Structuring of Rare-Earth Free L1-FeNi Magnets
Alexander Kovacs, Johann Fischbacher, Harald Oezelt +6
In this work we investigate the potential of tetragonal L1 ordered FeNi as candidate phase for rare earth free permanent magnets taking into account anisotropy values from rece…