The SDE is first transformed into a random ordinary differential equation. Several solution paths are then simulated to generate a large number of ordinary differential equations, and each of these is then solved using an EM algorithm type approach that was introduced in an earlier paper. The method is tested on two systems, the Lorenz96 and Lorenz63 models, and compared to a competitor method showing that the new approach can be faster and more accurate. There are some interesting ideas in the paper but I can't accept it for publication in its current form. The general approach seems reasonable, but there are some details of it that the authors don't really mention that I think need to be explored.

We introduce an additional randomization process, called Brownian sensing, based on the computation of stochastic integrals, which produces a Gaussian sensing matrix, for which good recovery properties are proven, independently on the number of sampling points N, even when the features are arbitrarily non-orthogonal.

Stochastic processes have many applications, including in finance and physics. It is an interesting model to represent many phenomena. Unfortunately the theory behind it is very difficult, making it accessible to a few'elite' data scientists, and not popular in business contexts. One of the most simple examples is a random walk, and indeed easy to understand with no mathematical background. However, time-continuous stochastic processes are always defined and studied using advanced and abstract mathematical tools such as measure theory, martingales, and filtration.

Stochastic processes have many applications, including in finance and physics. It is an interesting model to represent many phenomena. Unfortunately the theory behind it is very difficult, making it accessible to a few'elite' data scientists, and not popular in business contexts. One of the most simple examples is a random walk, and indeed easy to understand with no mathematical background. However, time-continuous stochastic processes are always defined and studied using advanced and abstract mathematical tools such as measure theory, martingales, and filtration.