Accurately modeling magnetic hysteresis plays a crucial role in develo** precise digital
twins for low-frequency electromagnetic systems. However, in large 3-D analysis systems …

This paper introduces a novel approach for modeling the dynamics of structural systems,
addressing challenges posed by uncertain boundary conditions and hysteresis forces. The …

Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating
optimal designs. Hitherto, deep learning-based methods employed to model hysteresis face …

Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and
identification are crucial for understanding and optimizing the behavior of various systems …

Optimization of rotating electrical machines is both time-and computationally expensive.
Because of the different parametrization, design optimization is commonly executed …

Hereby presented is a comparative analysis of different deep neural network models aimed
at simulating magnetic hysteresis under distorted excitation waveforms. Specifically feed …

The development of innovative materials, based on the modern technologies and
processes, is the key factor to improve the energetic sustainability and reduce the …

This paper presents a modular and scalable machine learning framework for multi-material
magnetic core loss modeling. The neural network framework is trained to predict core loss …

Designing an electromagnetic device, as with many other devices, is an inverse problem.
The issue is that the performance and some constraints on the inputs are provided but the …