Event

Fully Nonlinear Second Order PDEs and Their Numerical Solutions

Xiaobing Feng
Xiaobing Feng - Department of Mathematics of UTK

Abstract: Fully nonlinear PDEs are referred to the class of nonlinear PDEs which are nonlinear in the highest order derivatives of the unknown functions appearing in the equations,  they arise from many fields in science and engineering such as astrophysics, antenna design, differential geometry, geostrophic fluid dynamics, materials science, mathematical finance, meteorology, optimal transport, and stochastic control. This class of PDEs are known to be very difficult to study analytically and to approximate numerically. In this talk I will review and discuss some latest advances in developing efficient numerical methods for fully nonlinear second (and first) order PDEs such as tehe Monge-Ampere type equations  and Hamilton-Jacobi-Bellman equations.

Background materials on the viscosity solution theory for fully nonlinear PDEs will be briefly reviewed. The focus of the talk will be on discussing  various numerical  approaches/methods/ideas and their pros and cons for constructing numerical methods which can reliably approximate viscosity solutions of fully nonlinear second order PDEs. Numerical experiments and application problems as well as open problems in numerical fully nonlinear PDEs will also be presented.

Biography: Xiaobing Feng is a professor in Department of Mathematics of UTK and has served as an associate department head and Director of Graduate Studies.  Xiaobing obtained his Ph.D. from Purdue University in Computational and Applied Math in 1992 under the direction Jim Douglas, Jr. His primary research interest is numerical solutions of deterministic and  stochastic nonlinear PDEs which arise from various applications including fluid and solid mechanics, subsurface flow and poroelasticity, phase transition, forward and inverse scattering,  image processing, optimal control, general relativity and systems biology. 

Websitehttp://www.math.utk.edu/people/bio/Xiaobing/Feng

 

Last Updated: May 28, 2020 - 4:03 pm