In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.

通过结合 Thiele 方程法和微磁性模拟数据，我们开发了一个非常规模型，定量描述了自旋扭矩涡流纳米振荡器（STVO）的动力学。利用我们的分析模型解决 STVO 动力学问题，可以加速模拟速度达到与微磁性模拟相当的精度，其加速度达到9个数量级。我们通过模拟一个基于 STVO 的神经网络解决分类任务来展示我们的模型，并评估模型在输入信号电流强度和可能影响系统的噪声水平方面的性能。我们的方法有望加快 STVO 的神经形态计算设备设计，并大幅降低计算成本。

利用非常规数据驱动的 Thiele 方程方法模拟神经形态自旋电子学