Modelica is a freely available, object-oriented language for modeling of large, complex, and heterogeneous systems. It is suited for multi-domain modeling, for example, mechatronic models in robotics, automotive and aerospace applications involving mechanical, electrical, hydraulic control and state machine subsystems, process oriented applications and generation and distribution of electric power. Models in Modelica are mathematically described by differential, algebraic and discrete equations. No particular variable needs to be solved for manually. A Modelica tool will have enough information to decide that automatically. Modelica is designed such that available, specialized algorithms can be utilized to enable efficient handling of large models having more than one hundred thousand equations. Modelica is suited and used for hardware-in-the-loop simulations and for embedded control systems.

References in zbMATH (referenced in 128 articles )

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  1. Haid, Timo; Watzenig, Daniel; Stettinger, Georg: Analysis of the model-based corrector approach for explicit cosimulation (2022)
  2. Marzorati, Denise; Fernández, Joaquin; Kofman, Ernesto: Efficient connection processing in equation-based object-oriented models (2022)
  3. Olivier, Bryan; Verlinden, Olivier; Kouroussis, Georges: Comparison of X-T and X-X co-simulation techniques applied on railway dynamics (2022)
  4. Pour, Pouya Aghaei; Rodemann, Tobias; Hakanen, Jussi; Miettinen, Kaisa: Surrogate assisted interactive multiobjective optimization in energy system design of buildings (2022)
  5. Casella, Francesco; Bachmann, Bernhard: On the choice of initial guesses for the Newton-Raphson algorithm (2021)
  6. Chen, Weitao; Ran, Shenhai; Wu, Canhui; Jacobson, Bengt: Explicit parallel co-simulation approach: analysis and improved coupling method based on H-infinity synthesis (2021)
  7. Choi, Hee-Sun; An, Junmo; Han, Seongji; Kim, Jin-Gyun; Jung, Jae-Yoon; Choi, Juhwan; Orzechowski, Grzegorz; Mikkola, Aki; Choi, Jin Hwan: Data-driven simulation for general-purpose multibody dynamics using deep neural networks (2021)
  8. Huo, Zenan; Mei, Gang; Xu, Nengxiong: JuSFEM: a Julia-based open-source package of parallel smoothed finite element method (S-FEM) for elastic problems (2021)
  9. Kofman, Ernesto; Fernández, Joaquín; Marzorati, Denise: Compact sparse symbolic Jacobian computation in large systems of ODEs (2021)
  10. Körner, Philipp; Bendisposto, Jens; Dunkelau, Jannik; Krings, Sebastian; Leuschel, Michael: Integrating formal specifications into applications: the ProB Java API (2021)
  11. Abhyankar, Shrirang; Betrie, Getnet; Maldonado, Daniel Adrian; Mcinnes, Lois C.; Smith, Barry; Zhang, Hong: PETSc DMNetwork: a library for scalable network PDE-based multiphysics simulations (2020)
  12. Di Pietro, Franco; Fernández, Joaquín; Migoni, Gustavo; Kofman, Ernesto: Mixed-mode state-time discretization in ODE numerical integration (2020)
  13. Francesco Witte; Ilja Tuschy: TESPy: Thermal Engineering Systems in Python (2020) not zbMATH
  14. Goncharov, Sergey; Neves, Renato; Proença, José: Implementing hybrid semantics: from functional to imperative (2020)
  15. Kučera, Erik; Haffner, Oto; Drahoš, Peter; Cigánek, Ján; Štefanovič, Juraj; Kozák, Štefan: New software tool for modelling and control of discrete-event and hybrid systems using Petri nets (2020)
  16. Rozhdestvensky, Kirill; Ryzhov, Vladimir; Fedorova, Tatiana; Safronov, Kirill; Tryaskin, Nikita; Sulaiman, Shaharin Anwar; Ovinis, Mark; Hassan, Suhaimi: Computer modeling and simulation of dynamic systems using Wolfram SystemModeler (2020)
  17. Schweiger, G.; Nilsson, H.; Schoeggl, J.; Birk, W.; Posch, A.: Modeling and simulation of large-scale systems: a systematic comparison of modeling paradigms (2020)
  18. Pollok, Alexander; Klöckner, Andreas; Zimmer, Dirk: Psychological aspects of equation-based modelling (2019)
  19. Pothen, Alex; Ferdous, S. M.; Manne, Fredrik: Approximation algorithms in combinatorial scientific computing (2019)
  20. Skjong, Stian; Pedersen, Eilif: On the numerical stability in dynamical distributed simulations (2019)

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