We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm.

翻译：我们提出了一种基于噪声引导轨迹的系统辨识方法，用于从随机微分方程生成的观测数据中推断动力学结构。该方法能够处理多种噪声类型，包括噪声分量存在相关性的情形，并且可同时从轨迹中学习噪声水平与漂移项。我们通过多种数值测试展示了该学习算法的优越性能。