This REU program will provide undergraduate students with vigorous, motivating, and above all quality collaborative research experience in the areas of stochastic and deterministic partial differential equations and numerical analysis with an emphasis on the computational aspects. Additionally, students will develop and/or apply deep spatial learning algorithms to spatial processes resulting from partial differential equations. As a first objective, students will study and analyze novel and engaging topics such as the dynamics of scattering waves and propagation of nonlinear waves in non-uniform media. As a second objective, students will work on simulating spatial processes such as the spread of diseases or emission of pollution, which are modeled using partial differential equations with space-time white or color noise. Students will also produce predictive models and compare their outcomes to actual models of spatial processes. Understanding how to address each model computationally will have a broad impact on the students’ ability to tackle other mathematical models and be competitive as applicants to graduate programs as well as in industry. Moreover, students will be enriched academically with workshops in scientific writing, presentation skills, and graduate school readiness.
The University of Texas Rio Grande Valley