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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Ali, Qais Fischbacher, Johann Kovacs, Alexander Oezelt, Harald Gusenbauer, Markus Schrefl, Thomas University for Continuing Education Krems Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning Wiener Neustadt Austria University for Continuing Education Krems Department for Integrated Sensor Systems Wiener Neustadt Austria

ISBN: (纸本)9798350338362

A combined effect of defect thickness and externally applied field angle is studied for physics based modelling of coercivity. A single cubic grain with intrinsic properties of Nd2Fe14 B surrounded by a soft magnetic defect is used. Finite element based micromagnetic modelling is used to compute coercivity and magnetic microstructural parameters. A novel magnetic reversal process is reported, which is primarily driven by defect thickness. This work provides implications for future work to design better grain boundary engineered permanent magnets for applications in green technologies. © 2023 IEEE.

关键词： Permanent magnets

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Structural and micromagnetic modeling of the magnetic binder phase in WC-Co cemented carbides

Structural and micromagnetic modeling of the magnetic binder...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Breth, Leoni Fischbacher, Johann Kovacs, Alexander Oezelt, Harald Schrefl, Thomas Czettl, Christoph Kuehrer, Saskia Pachlhofer, Julia Schwarz, Maria Weirather, Thomas Brueckl, Hubert University for Continuing Education Krems Dept. Integrated Sensor Systems Wiener Neustadt Austria Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning University for Continuing Education Krems Wiener Neustadt Austria Ceratizit Austria GmbH R&d Cutting Tools Reutte Austria

ISBN: (纸本)9798350338362

The magnetic properties of tungsten carbide with cobalt as a binder have long been used in industry to characterize the microstructure of this hard metal. We present bottom-up structural and micromagnetic modeling of WC-Co, from which we obtain coercive fields that compare well to experimental data. Future work will use experimentally obtained microstructure information as input to the structural modeling. © 2023 IEEE.

关键词： Microstructure

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Modeling liquid-mediated interactions for close-to-substrate magnetic microparticle transport in dynamic magnetic field landscapes

arXiv

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arXiv 2025年

作者： Gusenbauer, Markus Huhnstock, Rico Kovacs, Alexander Oezelt, Harald Ehresmann, Arno Schrefl, Thomas Department for Integrated Sensor Systems University for Continuing Education Krems Krems3500 Austria University of Kassel Kassel34132 Germany Christian Doppler Laboratory for Magnet design through physics informed machine learning Wiener Neustadt2700 Austria

Understanding the on-chip motion of magnetic particles in a microfluidic environment is key to realizing magnetic particle-based Lab-on-a-chip systems for medical diagnostics. In this work, a simulation model is established to quantify the trajectory of a single particle moving close to a polymer surface in a quiescent liquid. The simulations include hydrodynamic, magnetostatic, and Derjaguin-Landau-Verwey-Overbeek (DLVO) interactions. They are applied to particle motion driven by a dynamically changing magnetic field landscape created by engineered parallel-stripe magnetic domains superposed by a homogeneous, time-varying external magnetic field. The simulation model is adapted to experiments in terms of fluid-particle interactions with the magnetic field landscape approximated by analytic equations under the assumption of surface charges. Varying simulation parameters, we especially clarify the impact of liquid-mediated DLVO interactions, which are essential for diagnostic applications, on the 3D trajectory of the particle. A comparison to experimental results validates our simulation approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetic bubbles

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Tuning the coercivity of permanent magnets by the combined effect of field angle and defect thickness

Tuning the coercivity of permanent magnets by the combined e...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Qais Ali Johann Fischbacher Alexander Kovacs Harald Oezelt Markus Gusenbauer Thomas Schrefl Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning University for Continuing Education Krems Wiener Neustadt Austria Department for Integrated Sensor Systems University for Continuing Education Krems Wiener Neustadt Austria

A combined effect of defect thickness and externally applied field angle is studied for physics based modelling of coercivity. A single cubic grain with intrinsic properties of Nd 2 Fe 14 B surrounded by a soft magnetic defect is used. Finite element based micromagnetic modelling is used to compute coercivity and magnetic microstructural parameters. A novel magnetic reversal process is reported, which is primarily driven by defect thickness. This work provides implications for future work to design better grain boundary engineered permanent magnets for applications in green technologies.

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Full-spin-wave-scaled stochastic micromagnetism for mesh-independent simulations of ferromagnetic resonance and reversal

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npj Computational Materials 2022年第1期8卷 315-323页

作者： Harald Oezelt Luman Qu Alexander Kovacs Johann Fischbacher Markus Gusenbauer Roman Beigelbeck Dirk Praetorius Masao Yano Tetsuya Shoji Akira Kato Roy Chantrell Michael Winklhofer Gergely T.Zimanyi Thomas Schrefl Department for Integrated Sensor Systems Danube University Krems2700Wiener NeustadtAustria Department of Physics University of CaliforniaDavisCA95616USA Christian Doppler Laboratory for Magnet design through physics informed machine learning 2700Wiener NeustadtAustria Institute for Analysis and Scientific Computing Vienna University of Technology1040WienAustria Toyota Motor Corp. Toyota City471-8572Japan Department of Physics University of YorkYorkYO105DDUK Institute for Biology and Environmental Sciences IBU Carl von Ossietzky University of Oldenburg26129OldenburgGermany

In this paper,we address the problem that standard stochastic Landau-Lifshitz-Gilbert(sLLG)simulations typically produce results that show unphysical mesh-size *** root cause of this problem is that the effects of spin-wave fluctuations are ignored in *** propose to represent the effect of these fluctuations by a full-spin-wave-scaled stochastic LLG,or FUSSS LLG *** FUSSS LLG,the intrinsic parameters of the sLLG simulations are first scaled by scaling factors that integrate out the spin-wave fluctuations up to the mesh size,and the sLLG simulation is then performed with these scaled *** developed FUSSS LLG by studying the Ferromagnetic Resonance(FMR)in Nd_(2)Fe_(14)B *** nominal scaling greatly reduced the mesh size dependence relative to *** then performed three tests and validations of our FUSSS LLG with this modified scaling.(1)We studied the same FMR but with magnetostatic fields included.(2)We simulated the total magnetization of the Nd_(2)Fe_(14)B cube.(3)We studied the effective,temperature-and sweeping rate-dependent coercive field of the *** all three cases,we found that FUSSS LLG delivered essentially mesh-size-independent results,which tracked the theoretical expectations better than unscaled *** by these successful validations,we propose that FUSSS LLG provides marked,qualitative progress towards accurate,high precision modeling of micromagnetics in hard,permanent magnets.

关键词： stochastic wave scaling

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Structural and micromagnetic modeling of the magnetic binder phase in WC-Co cemented carbides

Structural and micromagnetic modeling of the magnetic binder...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Leoni Breth Johann Fischbacher Alexander Kovacs Harald Oezelt Thomas Schrefl Christoph Czettl Saskia Kuehrer Julia Pachlhofer Maria Schwarz Thomas Weirather Hubert Brueckl Dept. Integrated Sensor Systems University for Continuing Education Krems Wiener Neustadt Austria Christian Doppler Laboratory for Magnet design through physics informed machine learning University for Continuing Education Krems Wiener Neustadt Austria Ceratizit Austria GmbH R&D Cutting Tools Reutte Austria

ISBN: (数字)9798350338362

ISBN: (纸本)9798350338379

关键词： Industries Tungsten Magnetic hysteresis Data models Microstructure Micromagnetics Cobalt

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Physics aware machine learning for micromagnetic energy minimization: recent algorithmic developments

arXiv

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arXiv 2024年

作者： Schaffer, Sebastian Schrefl, Thomas Oezelt, Harald Mauser, Norbert J. Exl, Lukas Research Platform MMM Mathematics - Magnetism - Materials University of Vienna Vienna Austria Wolfgang Pauli Institute Vienna Austria Faculty of Mathematics University of Vienna Vienna Austria Christian Doppler Laboratory for Magnet Design Through Physics-Informed Machine Learning University for Continuing Education Krems Wr. Neustadt Austria Department of Integrated Sensor Systems University for Continuing Education Krems Wr. Neustadt Austria

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 formulations of the transmission problems for the scalar and vector potential. To overcome the computational challenges posed by whole-space integrals, we reformulate these bounds on a finite domain, making the method more efficient and scalable for numerical simulation. Our approach utilizes an alternating optimization scheme for joint minimization of Brown’s energy bounds and the Gibbs free energy. The Cayley transform is employed to rigorously enforce the unit norm constraint, while R-functions are used to impose essential boundary conditions in the computation of magnetostatic fields. Our results highlight the potential of mesh-free Physics-Informed Neural Networks (PINNs) and Extreme learning machines (ELMs) when integrated with hard constraints, providing highly accurate approximations. These methods exhibit competitive performance compared to traditional numerical approaches, showing significant promise in computing magnetostatic fields and the application for energy minimization, such as the computation of hysteresis curves. This work opens the path for future directions of research on more complex geometries, such as grain structure models, and the application to large scale problem settings which are intractable with traditional numerical *** Codes 62P35, 68T07, 65Z05 © 2024, CC BY.

关键词： Magnetostatics

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Mixture of Experts-Enabled Parallel Scheduling and Processing for Vehicular Generative AI Services

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Xie, Gaochang Xiong, Zehui Xie, Renchao Deng, Xiumei Guo, Song Guizani, Mohsen Han, Zhu Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Nanjing211111 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Mohammad Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Foundation models (FMs) have revolutionized generative AI (GAI) lifecycle with their pre-trained intelligence capabilities. While the recent success of web-based models like GPT-4 has spurred interest in extending FMs to edge scenarios like the Internet of Vehicles (IoV), challenges such as data privacy, network congestion, and limited edge resource awareness hinder the direct application of cloud-based FMs. To address these challenges, this paper reconfigures the mixture of experts (MoE) architecture to deploy Transformer-based FM experts across vehicles for distributed GAI inference. Specifically, we propose an MoE-empowered vehicular FM system with two key innovations: a physical gating network that dynamically adapts to wireless environments, and a vehicle-to-vehicle (V2V) communication-based expert parallelism mechanism to enhance efficiency and resource utilization. Then, we formulate the communication, computation, and memory models for analyzing inference latency. Furthermore, to optimize the computational resource allocation for enhancing inference performance, we establish a Stackelberg game and propose the Gradient Ascent and Evolutionary Optimization-based Competitive Pricing and Allocation (GECPA) algorithm, which balances resource allocation and usage costs by combining the rapid convergence of gradient ascent with the broader exploration capability of evolutionary optimization. Simulation results demonstrate the superior parallel processing efficiency and reduced latency of the proposed MoE-based FM inference scheme. Compared with the best-performing benchmark algorithm, GECPA improves the average utility of infrastructure vehicles by up to 2.01 times, reduces inference latency by 16.58%, and increases the successful execution rate of GAI tasks by 10.66%, thereby achieving a better balance between efficiency and incentive compatibility in dynamic IoV environments. © 2015 IEEE.

关键词： Resource allocation

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Defect Manipulation for the Coercivity Enhancement of Nd-Fe-B Permanent Magnets

SSRN

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SSRN 2023年

作者： Ali, Qais Fischbacher, Johann Kovacs, Alexander Oezelt, Harald Gusenbauer, Markus Moustafa, Heisam Böhm, David Breth, Leoni Schrefl, Thomas Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning Department for Integrated Sensor Systems University for Continuing Education Krems 2 E Viktor Kaplan-Straße Wiener Neustadt2700 Austria Department for Integrated Sensor Systems University for Continuing Education Krems 2 E Viktor Kaplan-Straße Wiener Neustadt2700 Austria

The demand for rare-earth based permanent magnets is expected to rise for various climate friendly technologies. This motivates for the search of better designed permanent magnets with optimal use of rare-earth elements without compromising the key magnetic properties. We simulate Nd$_2$Fe$_{14}$B based permanent magnet to study the effects of microstructure on coercivity ($H_{\rm C}$). We demonstrate a fast method to compute the microstrucutral parameters in numerical micromagnetics. We report a novel magnetic reversal process, which is primarily driven by defect thickness. By selective replacement of the defect with a hard magnetic phase $H_{\rm C}$ is expected to rise by 1.5 times to that of single grain model with soft defect. This study is extended to a multigrain polycrystal model. Our systematic approach helps in identifying specific microstructural features pertinent for enhancement of coercivity. This work provides implications for future work to design improved defect engineered permanent magnets for green technologies. © 2023, The Authors. All rights reserved.

关键词： Coercive force

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Coercivity limits in nanoscale ferromagnets

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Physical Review B 2022年第21期105卷 214431-214431页

作者： Jeotikanta Mohapatra J. Fischbacher M. Gusenbauer M. Y. Xing J. Elkins T. Schrefl J. Ping Liu Department of Physics University of Texas at Arlington Arlington Texas 76019 USA Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning Viktor Kaplan-Straße 2E 2700 Wiener Neustadt Austria Department for Integrated Sensor Systems Danube University Krems Viktor Kaplan-Straße 2E 2700 Wiener Neustadt Austria

It has been a puzzle for a century about how “hard” (coercive) a ferromagnet can be. Seven decades ago, W. Brown gave his famous theorem to correlate coercivity of a ferromagnet to its magnetocrystalline anisotropy field. However, the experimental coercivity values are far below the calculated level given by the theorem, which is called Brown's Coercivity Paradox. The paradox has been considered to be related to the complex microstructures of the magnets in experiments because coercivity is an extrinsic property that is sensitive to any imperfections in the specimens. To date, coercivity cannot be predicted and calculated by quantitative modeling. In this investigation, we carried out a case study on the high magnetic coercivity of Co nanowires exceeding the magnetocrystalline anisotropy field as predicted by Brown's theorem. It is found that the aspect ratio and diameter of the nanocrystals have a strong effect on the coercivity. When the nanocrystals have an increased aspect ratio, the coercivity is significantly higher than the magnetocrystalline anisotropy field of a hcp Co crystal. Micromagnetic simulations give a coercivity aspect-ratio dependence that is well consistent with the experimental results. It is also revealed that a coercivity limit exists based on the geometrical structures of the nanocrystals that govern the demagnetizing process. The quantitative correlation obtained between the structure and coercivity enables material design of advanced permanent magnets in the future.

关键词： Magnetic anisotropy Magnetic nanoparticles Nanowires Magnetization measurements Micromagnetic modeling

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