Strayfield calculation for micromagnetic simulations using true periodic boundary conditions

Bruckner, Florian; Ducevic, Amil; Heistracher, Paul; Abert, Claas; Suess, Dieter

doi:10.1038/s41598-021-88541-9

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…This is crucial when simulating the microstructure of magnetic materials. The differential version of the corresponding Maxwell equations can be solved efficiently by means of the Fast Fourier Transfrom, which intrinsically fulfills the proper periodic boundary conditions [28].…”

Section: Demagnetization Fieldmentioning

confidence: 99%

magnum.np - A PyTorch based GPU enhanced Finite Difference Micromagnetic Simulation Framework for High Level Development and Inverse Design

Bruckner

Koraltan

Abert

et al. 2023

Preprint

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magnum.np is a micromagnetic finite-difference library completely based on the tensor library PyTorch. The use of such a high level library leads to a highly maintainable and extensible code base which is the ideal candidate for the investigation of novel algorithms and modeling approaches. On the other hand magnum.np benefits from the devices abstraction and optimizations of PyTorch enabling the efficient execution of micromagnetic simulations on a number of computational platforms including GPU and potentially TPU systems. We demonstrate a competitive performance to state-of-the art micromagnetic codes such a mumax3 and show how our code enables the rapid implementation of new functionality. Furthermore, handling inverse problems becomes possible by using PyTorch's autograd feature.

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Section: Demagnetization Fieldmentioning

confidence: 99%

magnum.np - A PyTorch based GPU enhanced Finite Difference Micromagnetic Simulation Framework for High Level Development and Inverse Design

Bruckner

Koraltan

Abert

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Field Termsmentioning

confidence: 99%

magnum.np: a PyTorch based GPU enhanced finite difference micromagnetic simulation framework for high level development and inverse design

Bruckner¹,

Koraltan²,

Abert³

et al. 2023

Sci Rep

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Abstractmagnum.np is a micromagnetic finite-difference library completely based on the tensor library PyTorch. The use of such a high level library leads to a highly maintainable and extensible code base which is the ideal candidate for the investigation of novel algorithms and modeling approaches. On the other hand magnum.np benefits from the device abstraction and optimizations of PyTorch enabling the efficient execution of micromagnetic simulations on a number of computational platforms including graphics processing units and potentially Tensor processing unit systems. We demonstrate a competitive performance to state-of-the-art micromagnetic codes such as mumax3 and show how our code enables the rapid implementation of new functionality. Furthermore, handling inverse problems becomes possible by using PyTorch’s autograd feature.

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“…Here, we propose using auto-differentiation tools from DL frameworks (Torch 28 )-not to train a neural network for generalizing across multiple spectra, but to perform highly efficient fitting on every individual spectrum. Although similar concepts have been recently applied to curve fitting in other fields, [29][30][31] to the best of our knowledge, this approach has not been previously applied for metabolite fitting in MRSI.…”

Section: Introductionmentioning

confidence: 99%

TensorFit: A torch‐based tool for ultrafast metabolite fitting of large MRSI data sets

Turco,

Capiglioni,

Weng

et al. 2024

Magnetic Resonance in Med

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PurposeTo introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI data based on Torch's auto‐differentiation and optimization framework.MethodsTensorFit was implemented in Python based on Torch's auto‐differentiation to fit individual metabolites in MRS spectra. The underlying time domain and/or frequency domain fitting model is based on a linear combination of metabolite spectroscopic response. The computational time efficiency and accuracy of TensorFit were tested on simulated and in vivo MRS data and compared against TDFDFit and QUEST.ResultsTensorFit demonstrates a significant improvement in computation speed, achieving a 165‐times acceleration compared with TDFDFit and 115 times against QUEST. TensorFit showed smaller percentual errors on simulated data compared with TDFDFit and QUEST. When tested on in vivo data, it performed similarly to TDFDFit with a 2% better fit in terms of mean squared error while obtaining a 169‐fold speedup.ConclusionTensorFit enables fast and robust metabolite fitting in large MRSI data sets compared with conventional metabolite fitting methods. This tool could boost the clinical applicability of large 3D MRSI by enabling the fitting of large MRSI data sets within computation times acceptable in a clinical environment.

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Strayfield calculation for micromagnetic simulations using true periodic boundary conditions

Cited by 11 publications

References 12 publications

magnum.np - A PyTorch based GPU enhanced Finite Difference Micromagnetic Simulation Framework for High Level Development and Inverse Design

magnum.np - A PyTorch based GPU enhanced Finite Difference Micromagnetic Simulation Framework for High Level Development and Inverse Design

magnum.np: a PyTorch based GPU enhanced finite difference micromagnetic simulation framework for high level development and inverse design

TensorFit: A torch‐based tool for ultrafast metabolite fitting of large MRSI data sets

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