Past Event: Oden Institute Seminar

jInv - A Flexible Julia Framework for Parallel PDE Constrained Optimization

Lars Ruthotto, Professor, Emory University

Abstract

jInv is a Julia framework for the solution of large-scale PDE constrained optimization problems. It supports linear and nonlinear PDE constraints and provides many commonly used tools in inverse problems such as different misfit functions, regularizers, and efficient methods for numerical optimization. Also, it provides easy access to both iterative and direct linear solvers for solving linear PDEs. A main feature of jInv is the provided easy access to parallel and distributed computation supporting a variety of computational architectures: from a single laptop to large clusters of cloud computing engines. Being written in the high-level dynamic language Julia, it is easily extendable and yet fast. I will outline jInv's potential using examples from geophysical imaging with both linear and nonlinear PDE forward models. Also, I will give an overview over current work towards reduced order modeling and stochastic optimization.