NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics
We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The li...
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| Published in | npj computational materials Vol. 11; no. 1; pp. 193 - 10 |
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| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.01.2025
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2057-3960 2057-3960 |
| DOI | 10.1038/s41524-025-01688-1 |
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| Abstract | We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends. |
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| AbstractList | We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends. We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends.We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends. Abstract We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends. |
| ArticleNumber | 193 |
| Author | Bruckner, F. Koraltan, S. Lang, M. Kraft, R. Abert, C. Voronov, A. Allayarov, R. Holt, S. Suess, D. Schrefl, T. Pathak, S. A. Fangohr, H. Chumak, A. Flauger, P. |
| Author_xml | – sequence: 1 givenname: C. surname: Abert fullname: Abert, C. email: claas.abert@univie.ac.at organization: Faculty of Physics, University of Vienna – sequence: 2 givenname: F. surname: Bruckner fullname: Bruckner, F. organization: Faculty of Physics, University of Vienna – sequence: 3 givenname: A. surname: Voronov fullname: Voronov, A. organization: Faculty of Physics, University of Vienna, Vienna Doctoral School of Physics, University of Vienna – sequence: 4 givenname: M. surname: Lang fullname: Lang, M. organization: Max Planck Institute for the Structure and Dynamics of Matter, Center for Free-Electron Laser Science – sequence: 5 givenname: S. A. surname: Pathak fullname: Pathak, S. A. organization: Max Planck Institute for the Structure and Dynamics of Matter, Center for Free-Electron Laser Science – sequence: 6 givenname: S. surname: Holt fullname: Holt, S. organization: Max Planck Institute for the Structure and Dynamics of Matter, Center for Free-Electron Laser Science – sequence: 7 givenname: R. surname: Kraft fullname: Kraft, R. organization: Faculty of Physics, University of Vienna, Vienna Doctoral School of Physics, University of Vienna – sequence: 8 givenname: R. surname: Allayarov fullname: Allayarov, R. organization: Faculty of Physics, University of Vienna – sequence: 9 givenname: P. surname: Flauger fullname: Flauger, P. organization: Faculty of Physics, University of Vienna – sequence: 10 givenname: S. surname: Koraltan fullname: Koraltan, S. organization: Institute of Applied Physics, TU Wien – sequence: 11 givenname: T. surname: Schrefl fullname: Schrefl, T. organization: Department for Integrated Sensor Systems, University for Continuing Education Krems – sequence: 12 givenname: A. surname: Chumak fullname: Chumak, A. organization: Faculty of Physics, University of Vienna – sequence: 13 givenname: H. surname: Fangohr fullname: Fangohr, H. organization: Max Planck Institute for the Structure and Dynamics of Matter, Center for Free-Electron Laser Science, University of Southampton – sequence: 14 givenname: D. surname: Suess fullname: Suess, D. organization: Faculty of Physics, University of Vienna |
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| Keywords | Ferromagnetism Spintronics |
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| Snippet | We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning... Abstract We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine... |
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| SubjectTerms | 639/766/119/1001 639/766/119/2793 Algorithms Characterization and Evaluation of Materials Chemistry and Materials Science Computational Intelligence Computer applications Energy Finite difference method Interfaces Inverse problems Machine learning Materials Science Mathematical analysis Mathematical and Computational Engineering Mathematical and Computational Physics Mathematical Modeling and Industrial Mathematics Open source software Optimization techniques Python Simulation Source code Tensors Theoretical Variables |
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| Title | NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics |
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