Title: Improved Long-Term Prediction of Chaos Using Reservoir Computing Based on Stochastic Spin-Orbit Torque Devices Show Chinese title

Title: 基于随机自旋轨道扭矩器件的储备池计算在混沌长期预测中的改进

Abstract: Predicting chaotic systems is crucial for understanding complex behaviors, yet challenging due to their sensitivity to initial conditions and inherent unpredictability. Probabilistic Reservoir Computing (RC) is well-suited for long-term chaotic predictions by handling complex dynamic systems. Spin-Orbit Torque (SOT) devices in spintronics, with their nonlinear and probabilistic operations, can enhance performance in these tasks. This study proposes an RC system utilizing SOT devices for predicting chaotic dynamics. By simulating the reservoir in an RC network with SOT devices that achieve nonlinear resistance changes with random distribution, we enhance the robustness for the predictive capability of the model. The RC network predicted the behaviors of the Mackey-Glass and Lorenz chaotic systems, demonstrating that stochastic SOT devices significantly improve long-term prediction accuracy. Show Chinese abstract

Abstract: 预测混沌系统对于理解复杂行为至关重要，但由于其对初始条件的敏感性和固有的不可预测性，这一任务具有挑战性。 概率共振计算（RC）通过处理复杂动态系统，非常适合长期混沌预测。 自旋轨道扭矩（SOT）器件在自旋电子学中，由于其非线性和概率性操作，可以提高这些任务的性能。 本研究提出了一种利用SOT器件的RC系统来预测混沌动力学。 通过用实现随机分布的非线性电阻变化的SOT器件模拟RC网络中的水库，我们增强了模型的预测能力的鲁棒性。 RC网络预测了Mackey-Glass和Lorenz混沌系统的的行为，证明了随机SOT器件显著提高了长期预测的准确性。