Articlenpj computational materials2025
NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics.
Article in npj computational materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Shape, strain and spin texture as a modern triad for magnetic materials.Nature nanotechnology · 2026Review
- Reconstruction of magnetic structures and material parameters with convolutional neural network and bias field-constrained micromagnetic relaxation.Scientific reports · 2025Article
- Inverse-design topology optimization of magnonic devices using level-set method.Npj spintronics · 2025Article
- NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics.npj computational materials · 2025Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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