JModelica is an extensible Modelica-based open source platform for optimization, simulation and analysis of complex dynamic systems. The main objective of the project is to create an industrially viable open source platform for optimization of Modelica models, while offering a flexible platform serving as a virtual lab for algorithm development and research. As such, provides a platform for technology transfer where industrially relevant problems can inspire new research and where state of the art algorithms can be propagated from academia into industrial use. is a result of research at the Department of Automatic Control, Lund University, and is now maintained and developed by Modelon AB in collaboration with academia. is distributed under the GPL v.3 license approved by the Open Source Initiative. at a glance: Model your systems using the object-oriented and equation-based language Modelica. Solve your complex simulation and optimization problems using state of the art numerical algorithms. Automate your work in the Python scripting environment. Visualize your results.

References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Marzorati, Denise; Fernández, Joaquin; Kofman, Ernesto: Efficient connection processing in equation-based object-oriented models (2022)
  2. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  3. Elsheikh, Atiyah; Wiechert, Wolfgang: The structural index of sensitivity equation systems (2018)
  4. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  5. Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; Zavala, Victor M.; Biegler, Lorenz T.: \textttpyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations (2018)
  6. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  7. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  8. Maree, Johannes Philippus; Imsland, Lars: Combined economic and regulatory predictive control (2016)
  9. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015) not zbMATH
  10. Andersson, Christian; Führer, Claus; Åkesson, Johan: Assimulo: a unified framework for ODE solvers (2015)
  11. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  12. Mynttinen, I.; Hoffmann, A.; Runge, E.; Li, P.: Smoothing and regularization strategies for optimization of hybrid dynamic systems (2015)
  13. Limebeer, D. J. N.; Perantoni, G.; Rao, A. V.: Optimal control of formula one car energy recovery systems (2014)
  14. Patterson, Michael A.; Rao, Anil V.: (\mathbbGPOPS-\mathbbII): a MATLAB software for solving multiple-phase optimal control problems using (hp)-adaptive Gaussian quadrature collocation methods and sparse nonlinear programming (2014)
  15. Pytlak, Radosław; Tarnawski, Tomasz; Fajdek, Bartłomiej; Stachura, Marcin: Interactive dynamic optimization server -- connecting one modelling language with many solvers (2014)
  16. Word, Daniel P.; Kang, Jia; Akesson, Johan; Laird, Carl D.: Efficient parallel solution of large-scale nonlinear dynamic optimization problems (2014)
  17. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)
  18. Åkesson, Johan; Ekman, Torbjörn; Hedin, Görel: Implementation of a modelica compiler using JastAdd attribute grammars (2010)

Further publications can be found at: