Michael Holst  
https://ccom.ucsd.edu/~mholst/ 
Distinguished Professor of Mathematics and Physics UC San Diego 


Mathematical and Numerical General Relativity
Reading Course and Seminar Don Estep Colorado State University Abstract: Continuous optimization, data assimilation, determining model sensitivity, un certainty quantification, and a posteriori estimation of computational error are fun damentally important problems in mathematical modeling of the physical world. There has been some substantial progress on solving these problems in recent years, and some of these solution techniques are entering mainstream computational sci ence. A powerful framework for tackling all of these problems rests on the notion of duality and an adjoint operator. In the first part of this short course, we will discuss duality, adjoint operators, and Green′s functions; covering both the theoretical un derpinnings and practical examples. We will motivate these ideas by explaining the fundamental role of the adjoint operator in the solution of linear problems, working both on the level of linear algebra and differential equations. This will lead in a natural way to the definition of the Green′s function. In the second part of the course, we will describe how a generalization of the idea of a Green′s function is connected to a powerful technique for a posteriori error analysis of finite element methods. This technique is widely employed to obtain accurate and reliable error estimates in “quantities of interest”. We will also discuss the use of these estimates for adaptive error control. Finally, in the third part of the course, we will describe some applications of these analytic techniques. In the first, we will use the properties of Green′s functions to improve the efficiency of the solution process for an elliptic problem when the goal is to compute multiple quantities of interest and/or to compute quantities of interest that involve globallysupported information such as average values and norms. In the latter case, we introduce a solution decomposition in which we solve a set of problems involving localized information, and then recover the desired information by combining the local solutions. By treating each computation of a quantity of interest independently, the maximum number of elements required to achieve the desired accuracy can be decreased significantly. Time permitting, we will also discuss applications to a posteriori estimation of the effects of operator splitting in a multiphysics problem, estimation of the effect of random variation in parameters in a deterministic model (without using MonteCarlo), and extensions to nonlinear problems. November 2011 EVO Streamed PDF Slides from the lecture may be found [ here ] PDF Notes from a closely related minicourse on the subject may be found [ here ] MP4/M4V Audio/Video capture of the Evohosted lecture may be found [ here (coming soon) ] [ Back to MNGR Seminar Series Schedule ] 