07-23, 15:45–16:15 (US/Eastern), Elm B
Delay differential equations (DDEs) are used to model dynamics with inherent time delays in different scientific areas; however, solving them numerically in an efficient way is hard. This talk demonstrates how the DifferentialEquations ecosystem allows to solve even complicated DDEs with a variety of different numerical algorithms.
Time delays are an inherent part of many dynamical systems in different scientific areas such as biology, physiology, chemistry, and control theory, suggesting to model these systems with delay differential equations (DDEs), i.e., differential equations including time delays. However, solving DDEs numerically in an efficient way is hard. In my talk I present DelayDiffEq.jl, a Julia package for solving DDEs. I show how it integrates into the DifferentialEquations ecosystem and makes use of the large number of numerical algorithms in OrdinaryDiffEq.jl for solving ordinary differential equations.
I'm a PhD student at the IT department and the Center for Interdisciplinary Mathematics (CIM) at Uppsala University, Sweden. For my master thesis at TU Munich, Germany, I studied a delay differential equation model from biology and, since Julia is my preferred scientific programming language, I started to contribute to the development of DelayDiffEq.jl. My research interests are uncertainty quantification in machine learning and differential equations.