16 Apr 2020 |
Registration for the workshop is available as part of your registration for the Annual Conference on Pharmacometics
Have you been waiting too long for your QSP simulations to finish? Do you want to start making use of advanced modeling features, like stochastic differential equations or partial differential equations? This tutorial will focus on getting users up to speed with using Pumas and Julia’s DifferentialEquations.jl for high-performance QSP modeling. The workshop is designed for both beginners and experts alike, allowing them to get acquainted with the differential equation modeling in the Julia language and going to a level of modeling that is appropriate for them. We will start by describing how to define event-driven differential equations in the Julia programming language, and move to showcase how to make use of features like automatic parallelism, global parameter optimization with adjoint sensitivities for fast gradients, and finally advanced model types like stochastic differential equations and differential-algebraic equations. Together, users will get a broad sense of how to implement their QSP models using the Julia programming language in a manner that utilizes the fastest, most parallel, and most expressive set of tools available in the language.
Christopher Rackauckas, Vaibhav Dixit, Patrick Mogenson and Andreas Noack.
Agenda to be announced soon.
Main registration site for ACoP11