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Jean Sexton

jean sexton
Jean Sexton
Postdoctoral Scholar

Affiliation and Research Interests

I am a postdoctoral researcher in the Center for Computational Sciences and Engineering (CCSE) in the Computational Research Division of the Computing Sciences Directorate at the Lawrence Berkeley National Laboratory. I am interested in improving efficiency for simulations involving time integration methods.

 I am interested in improving the efficiency of simulations involving time integration methods. My current focus is on the Nyx N-body hydro code for computational cosmology.

Cosmological simulations allow us to investigate physical parameters which are not directly observable. Specifically, I am improving the numerical coupling between the hydrodynamic solver and the integration of the heating and cooling source terms as well as improving the overall algorithm efficiency. More efficient numerical methods and improved parallelism allow for more highly refined and complex models.

I have implemented a spectral deferred correction coupling strategy (which accounts for the comoving frame) in order to improve Nyx's heating-cooling integration efficiency and robustness. New improvements include updates to Nyx's use of the CVODE integration C-Fortran2003 interface, including an option to better use the most advanced version of CVODE by focusing solely on the C implementation.

I am currently working on porting these new implementations to hybrid CPU/GPU architectures by using CUDA to offload work to the GPU. This GPU implementation has been tested in standalone drivers to focus on the heating-cooling integration process, and is currently being added to the general use Nyx code.

Previous Work

I received my doctorate in Computational and Applied Mathematics from Southern Methodist University in late 2017. My dissertation research fits broadly in the applied mathematics fields of scientific computing and numerical analysis. Specifically, I focused on the development of numerical methods for the time integration of problems with multiple characteristic time scales. These methods are motivated by multiphysics, multiscale real-world application problems which are constructed by coupling physical processes with potential disparate length and time scales together. I developed a family of efficient, fully coupled fourth-order multirate method with comparable stability properties to leading existing third-order multirate methods. These methods were based on existing Recursive Flux-Splitting Multirate methods using Generalized Additive Runge-Kutta theory to analyze order conditions.