dae4py - python implementation of solvers for differential algebraic equations (DAE's)

This repository is a collection of examples and solvers for the course Differential Algebraic Equations I & II.

Fixed step-size implementations

Two fixed step-size implementations are given

• Implicit Runge-Kutta methods
• Backward differentation formulas (BDF)

Variable step-size Radau IIA methods

A variable step-size implementation of the Radau IIA method with odd number of stages is given in radau.py.

Python wrapper for old fortran solvers.

• Four-stage Radau IIA method pside.f of de Swart, Lioen and van der Veen.
• 3 stage Radau IIA method radau.f of Ernst Hairer.
• 3, 5 and 7 stage Radau IIA method radau.f of Ernst Hairer.
• BDF methods of Linda Petzold
  • ddassl.f.
  • ddaskr.f (not implemented yet).
  • ddaspk.f (not implemented yet).

Examples

All example discussed in the lecture are implemented here.

Install

• unix

  python -m venv myvenv
  source myvenv/bin/activate
  python -m pip install .

• windows

  python -m venv myvenv
  \myvenv\Scripts\activate.bat
  python -m pip install .

  Make sure that you are using the python interpreter of the virtual environment via

  python -c "import os, sys; print(os.path.dirname(sys.executable))"

  Compiling the fortran solvers on windows is by far not trivial. A simple and robust approach is to use WSL2 with Ubuntu.

• MacOS

  tbd