October 28, 2019 - November 1, 2019
The development and the application of software that relies on certain mathematical and statistical algorithms is widespread in institutes of all sections of the Leibniz Association. Applications are the statistical analysis of experimental and observational data, the simulation of complex models or the optimization of engineering devices.
The second PhD Summer School of the Leibniz Network “Mathematical Modeling and Simulation” wants to provide an introduction and an overview about two modern open source programming languages for science and statistics, namely R and Julia, which offer powerful tools for scientists. Even more, R and Julia are interoperable and one can profit from the best of both languages in everyone’s favourite language. R and Julia are presented with respect to the following criteria:
- main application areas (e.g., R for statistics, …)
- readability & learnability
- reproducibility of research
- interoperability with other open source software
- real world simulations & use as a scripting language (setup, parameter variation, …)
- sustainability in the sense of software engineering
- efficiency in time-critical applications
- parallelization of large-scale problems
The morning sessions of the summer school will be devoted to tutorials on various topics. The afternoon sessions are intended to be problem-oriented and practical.
Introductory morning sessions are planned to cover the following topics:
Complex 0: Reproducibility, version control, dynamic documents
Complex 1: The R environment for statistical computing and graphics
Complex 2: The Julia programming language – design principles and challenges
Complex 3: R and Julia in action: how they help to solve mathematical/statistical problems from the applied sciences
We plan the summer school to be problem-oriented, a concept which greatly worked in the previous summer school 2018. Ideally, we would center the afternoon discussions around problems and mathematical software pieces on which participants are actually working at. Possible problems could be: statistical analysis of experimental data, modelling by systems of nonlinear ordinary differential equations, partial differential equations, etc. We therefore ask participants to provide information on the topic and field of their PhD thesis as well as for introductory references on their problems that they are interested in. If possible we would like to ask participants to provide preliminary code pieces of mathematical algorithms, which they are trying to implement, preferably not later than the end of July. Statistical problems should include realistic example data.
Participants are asked to bring their own laptops.
- Patricio Farrell (WIAS),
- Jürgen Fuhrmann (WIAS),
- Alexander Linke (WIAS),
- Jörg Polzehl (WIAS),
- Chris Rackauckas (JuliaLab / MIT)
- Karsten Tabelow (WIAS)