Machine Learning and Dynamical SystemsApply
The student will work on machine learning techniques applied to the study of dynamical systems. Specifically, the student will use different Neural Network architectures to approximate the equations governing a given system's evolution. This evolution may be intrinsically random, or we may add randomness to contemplate the possibility of approximating a large dimensional system by a low dimensional one. He will also test this methodology in several applications and report his results to get familiar with the problem and its input data.
Program - Applied Mathematics and Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link - https://stochasticnumerics.kaust.edu.sa/Pages/Home.aspx
Field of Study - Computational and Applied Mathematics
Desired Project Deliverables
As the main project deliverable, we expect a scientific report describing the methodology developed in the internship and its numerical use in various applications. The working environment the student will use should include a GIT repository for all project-related materials to facilitate proper verification and feedback processes. These materials include, among others, the codes and the saved input-outputs corresponding to all tested cases