  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.
