Neural oscillators for magnetic hysteresis modeling

TL;DR

This paper introduces HystRNN, a neural oscillator model based on differential equations, designed to accurately capture and predict complex magnetic hysteresis behavior, including generalization to untrained scenarios.

Contribution

The paper presents a novel neural oscillator model, HystRNN, that effectively models magnetic hysteresis and outperforms traditional RNNs in capturing complex, nonlinear hysteresis patterns.

Findings

HystRNN accurately predicts hysteresis in magnetic materials.

HystRNN generalizes well to untrained hysteresis scenarios.

Neural oscillators outperform traditional RNNs in modeling hysteresis.

Abstract

Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We develop an ordinary differential equation-based recurrent neural network (RNN) approach to model and quantify the hysteresis, which manifests itself in sequentiality and history-dependence. Our neural oscillator, HystRNN, draws inspiration from coupled-oscillatory RNN and phenomenological hysteresis models to update the hidden states. The performance of HystRNN is evaluated to predict generalized scenarios, involving first-order reversal curves and minor loops. The findings show the ability of HystRNN to generalize its behavior to previously untrained regions, an essential feature that hysteresis models must have. This research highlights the advantage of neural oscillators over the traditional RNN-based methods in capturing complex hysteresis patterns in magnetic materials, where traditional rate-dependent methods are inadequate to capture intrinsic nonlinearity.