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Found 4 papers, 2 papers with code
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores
The MagNet Challenge 2023 calls upon competitors to develop data-driven models for the material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferrite cores.
Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly.
Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning
In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles.
Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control
An intelligent controller example based on the deep deterministic policy gradient algorithm which controls a series DC motor is presented and compared to a cascaded PI-controller as a baseline for future research.
