Applying Machine Learning to Parameter Estimation in the Lorenz System

We experiment -with the use of machine learning to solve the problem of parameter estimation in the Lorenz System. There are three unknown parameters in the Lorenz equations which govern the dynamics. We generated data with added noise and different initial conditions for the system, which we will use to estimate the value of the parameters (beta, rho, sigma). We make use of gradient descent, a common optimization algorithm used in machine learning, to find the optimal parameters for a noisy dataset, and we discuss its effectiveness in estimating the values of the parameters.