Summary of Magnetic Hysteresis Modeling with Neural Operators, by Abhishek Chandra et al.
Magnetic Hysteresis Modeling with Neural Operators
by Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Signal Processing (eess.SP); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for modeling magnetic hysteresis is proposed, leveraging neural operators to learn a mapping between magnetic fields. This addresses the generalization challenge faced by deep learning-based methods in predicting novel first-order reversal curves and minor loops. The authors employ three neural operators: deep operator network, Fourier neural operator, and wavelet neural operator, which demonstrate improved performance compared to traditional neural recurrent methods on various metrics. Additionally, a rate-independent Fourier neural operator is introduced to predict material responses at different sampling rates from those used during training. Numerical experiments showcase the efficiency of neural operators in modeling magnetic hysteresis under varying magnetic conditions. The findings highlight the benefits of using neural operators for characterizing magnetic material-based devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to model how magnets behave when exposed to different magnetic fields. They used special computer algorithms called neural operators to learn about the relationship between magnetic fields and the behavior of the magnet. This helps them predict how the magnet will react in new situations, which is important for designing better devices that use magnets. The researchers tested their approach using three different types of neural operators and found that it worked better than other methods they tried. They also showed that their method can work with different sampling rates, which means it can be used to study how magnets behave under different conditions. |
Keywords
» Artificial intelligence » Deep learning » Generalization
