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.

中文翻译：

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使用基于随机自旋轨道扭矩装置的储层计算改进混沌的长期预测


预测混沌系统对于理解复杂行为至关重要，但由于它们对初始条件的敏感性和固有的不可预测性，因此具有挑战性。概率储层计算 （RC） 非常适合通过处理复杂的动态系统进行长期混沌预测。自旋电子学中的自旋轨道扭矩 （SOT） 器件具有非线性和概率操作，可以提高这些任务的性能。本研究提出了一种利用 SOT 设备预测混沌动力学的 RC 系统。通过使用 SOT 器件模拟 RC 网络中的储层，实现随机分布的非线性电阻变化，我们增强了模型预测能力的鲁棒性。RC 网络预测了 Mackey-Glass 和 Lorenz 混沌系统的行为，表明随机 SOT 设备显著提高了长期预测的准确性。