# References

## Python

Assimulo offers 14 solvers with good documentation for explicit and implicit problems.

## Julia

- DifferentialEquations.jl offers a unified interface to about 300 different solvers from about a dozen different categories for a large range of problems. It wraps many existing open-source and commercial solvers, that have been implemented in C++ or Fortran and adds a growing number of native Julia solvers, many of them state-of-the-art.
- ModelingToolkit.jl is an acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations.
- KiteModels.jl implements kite models, connected to a tether for airborne wind energy applications. It uses the same algorithms as this tutorial, but it is not (yet) using ModelingToolkit.
- Working with Julia projects A must-read before creating your first project.

## Scientific papers

- Uwe Fechner, Rolf van der Vlugt, Edwin Schreuder, Roland Schmehl. (2015).
*Dynamic Model of a Pumping Kite Power System*describes the tether model used in this tutorial, but also a model of a complete kite power system with experimental validation. Renewable Energy. Preprint. - Yingbo Ma, Shashi Gowda, Ranjan Anantharaman, Chris Laughman, Viral Shah, and Chris Rackauckas. (2021).
*ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling.* - Rackauckas, Christopher and Nie, Qing (2017).
*DifferentialEquations.jl–a performant and feature-rich ecosystem for solving differential equations in Julia}*Journal of Open Research Software. - D.F. Duda1, H. Fuest, T. Islam, T. Ostermann, D. Moormann1. (2022).
*Hybrid modeling approach for the tether of an airborne wind energy system*CEAS Aeronautical Journal.