ArticleScientific reports2023
magnum.np: a PyTorch based GPU enhanced finite difference micromagnetic simulation framework for high level development and inverse design.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Magnetically programmable surface acoustic wave filters: device concept and predictive modeling.Npj spintronics · 2026Article
- Reconstruction of magnetic structures and material parameters with convolutional neural network and bias field-constrained micromagnetic relaxation.Scientific reports · 2025Article
- Tailoring the energy landscape of a bloch point domain wall with curvature.Nature communications · 2025Article
- Micromagnetic simulation and optimization of spin-wave transducers.Scientific reports · 2025Article
- Micromagnetic simulations with periodic strayfield calculation of soft magnetic composite-materials.Scientific reports · 2025Article
- NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics.npj computational materials · 2025Article
- Steerable current-driven emission of spin waves in magnetic vortex pairs.Science advances · 2024Article
- Dipolar skyrmions and antiskyrmions of arbitrary topological charge at room temperature.Nature physics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.