Dynamical systems, complex systems & nonlinear dynamics – Summer of Code

Agents.jl

Difficulty: Medium to Hard.

Length: 175 to 350 hours depending on the project.

Agents.jl is a pure Julia framework for agent-based modeling (ABM). It has an extensive list of features, excellent performance and is easy to learn, use, and extend. Comparisons with other popular frameworks written in Python or Java (NetLOGO, MASON, Mesa), show that Agents.jl outperforms all of them in computational speed, list of features and usability.

In this project, contributors will be paired with lead developers of Agents.jl to improve Agents.jl with more features, better performance, and overall higher polish. We are open to discuss with potential candidate a project description and outline for it!

Possible features to implement are:

Pre-requisite: Having already contributed to a Julia package either in JuliaDynamics or of sufficient relevance to JuliaDynamics.

Recommended Skills: Familiarity with agent based modelling, Agents.jl and Julia's Type System. Background in complex systems, sociology, or nonlinear dynamics is not required but would be advantageous.

Expected Results: Well-documented, well-tested useful new features for Agents.jl.

Mentors: George Datseris.

DynamicalSystems.jl

Difficulty: Easy to Medium to Hard, depending on the project.

Length: 175 to 350 hours, depending on the project.

DynamicalSystems.jl is an award-winning Julia software library for dynamical systems, nonlinear dynamics, deterministic chaos, and nonlinear time series analysis. It has an impressive list of features, but one can never have enough. In this project, contributors will be able to enrich DynamicalSystems.jl with new algorithms and enrich their knowledge of nonlinear dynamics and computer-assisted exploration of complex systems.

We do not outline possible projects here, and instead we invite interested candidates to reach out to one of the developers of DynamicalSystems.jl or its subpackages to devise a project outline. We strongly welcome candidates that already have potential project ideas in mind. To get ideas of possible projects we recommend having a look at the list of the open issues in the sub-packages of DynamicalSystems.jl.

Pre-requisite: Having already contributed to a Julia package either in JuliaDynamics or of sufficient relevance to JuliaDynamics.

Recommended Skills: Familiarity with nonlinear dynamics and/or differential equations and/or data analysis and the Julia language.

Expected Results: Well-documented, well-tested new algorithms for DynamicalSystems.jl.

Mentors: George Datseris