Numerical Analysis and Scientific Computing
Catalin Trenchea (Professor, PhD)
Dr. Trenchea’s expertise lies in the numerical analysis of semidiscrete and fully discrete space-time discretizations of control problems, convergence and error estimates, and the development of numerical algorithms for finding the optimal solutions. He is an expert on control theory in the abstract infinite dimensional space framework, the use of analysis and control theory for proving existence of optimal solutions (i.e., solutions that minimize the cost functional and satisfy the state equation), and getting necessary conditions of optimality for the continuous control problem.
Ivan Yotov (Professor, PhD)
Dr. Yotov’s research interests are in the numerical analysis and solution of partial differential equations and large scale scientific computing with applications to fluid flow and transport. His current research focus is on the design and analysis of accurate multiscale adaptive discretization techniques (mixed finite elements, finite volumes, finite differences) and efficient linear and nonlinear iterative solvers (domain decomposition, multigrid, Newton-Krylov methods) for massively parallel simulations of coupled multiphase porous media and surface flows. Other areas of research interest include estimation of uncertainty in stochastic systems and mathematical and computational modeling for biomedical applications. Dr. Yotov is also an adjunct faculty at the McGowan Institute for Regenerative Medicine.
John Burkardt (Part-Time Faculty)
John Burkardt's research has usually involved collaboration with numerical analysts. The scientist usually has a general idea of how to solve a problem, but John's task is to design and implement a corresponding computer program. The program accepts a description of the problem to be solved and creates reports or pictures of the results. Since this is usually an experimental process, the program must be repeatedly corrected or refined. This is done by working with the scientist to choose test cases, decide whether the program is working effectively, find remedies when the program fails, and search for improvements that make the program more accurate, faster, or able to solve a wider range of problems. His research projects have addressed the problem found here.
Many of the research programs he has worked on are available at https://people.sc.fsu.edu/~jburkardt/.
Michael Neilan (Professor, PhD)
Dr. Neilan's research interests include finite element methods and their convergence analysis for fully nonlinear partial differential equations. His current focus is on the construction and analysis of reliable and efficient numerical methods for the Monge-Ampere equation though the use of simple and practical finite elements. Other research interests of his include the numerical approximation of fluid flow (Stokes/Navier Stokes/Brinkman), the design and implementation of fourth and sixth order elliptic PDEs that arise in, e.g., plate bending and phase-field problems, the theory and construction of nonconforming finite element methods, and singular perturbation problems.
William Layton (Professor, PhD)
Dr. Layton's research involves modeling the large eddies (such as storm fronts, hurricanes and tornadoes in the atmosphere) in turbulent flow, predicting their motion in computational experiments and validating mathematically the large eddy models and algorithms developed. Current approaches to LES seem to be presently confronting some barriers to resolution, accuracy and predictability. It seems likely that many of these barriers can be traced to the mathematical foundation of the models used, the boundary conditions imposed and the algorithms employed for the simulations. The goal of Dr. Layton's research is to develop these mathematical foundations as a guide for practical computation. This research promises to make it possible to extend the range of accuracy and reliability of predictions important to applications, such as those described above, where technological progress requires confronting turbulence! In all these aspects of analysis, modeling, algorithm development, numerical analysis and experimentation, common themes include: Mathematical analysis as a guide to practical computation and Numerical analysis as a guide to understanding phenomena rather than "solving equations".