arXiv Machine Learning July 28, 2026 By Junyi Li, Tim Foissner, Floran Martin, Antti Piippo, Marko Hinkkanen

Gradient Networks for Universal Magnetic Modeling of Synchronous Machines

Read the original on arXiv Machine Learning →

arXiv:2602. 14947v2 Announce Type: replace-cross Abstract: This paper presents a physics-constrained neural network framework for dynamic modeling of saturable synchronous machines, including spatial harmonics.

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arXiv AI

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Accounting for Hysteresis and Eddy Currents in Finite Element Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network

arXiv:2607. 14321v1 Announce Type: cross Abstract: Incorporating hysteresis and eddy currents into finite element simulations of laminated-core electrical machines is computationally challenging.

By Florent Purnode, Louis Denis, Fran\c{c}ois Henrotte, Gilles Louppe, Christophe Geuzaine

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AFT Neural Function Approximators for 1D Nonlinear Force Laws

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arXiv Machine Learning

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A Physics-Informed Hybrid Neural Operator for Transient Magnetization Prediction in Power Magnetics

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arXiv Machine Learning

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Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets

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Certified Feedforward Tracking for Unknown Nonlinear Systems via Invertible Neural Networks

arXiv:2608. 06419v1 Announce Type: cross Abstract: In this paper, we address the certification of datadriven feedforward control for periodic tracking of unknown nonlinear systems under partial state measurements.

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arXiv AI

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Physics-Informed Broad Learning System: An Efficient Backpropagation-Free Framework for Solving Partial Differential Equations

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