Difficulty: Medium to Hard.
Length: 350 hours.
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. Possible features to implement are:
Automatic performance increase of mixed-agent models by eliminating dynamic dispatch on the stepping function
GPU support in Agents.jl
New type of space representing a planet, which can be used in climate policy or human evolution modelling, and new interface for an overarching ABM composed of several smaller ABMs
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.
Difficulty: Easy to Medium, depending on the algorithms chosen to implement.
Length: 175 hours.
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.
Possible projects are summarized in the wanted-features of the library
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