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


Difficulty: Easy to Medium.

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 students will be paired with lead developers of Agents.jl to improve Agents.jl with more features, better performance, and overall higher polish. Possible features to implement are:

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.

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

Mentors: George Datseris, Tim DuBois


Difficulty: Easy to Hard, depending on the algorithm chosen

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

Possible projects are summarized in the wanted-features of the library

Examples include but are are not limited to:

and many more.

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

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

Mentors: George Datseris