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 93 articles )

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  1. 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)
  2. 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)
  3. Di Pietro, Franco; Fernández, Joaquín; Migoni, Gustavo; Kofman, Ernesto: Mixed-mode state-time discretization in ODE numerical integration (2020)
  4. Francesco Witte; Ilja Tuschy: TESPy: Thermal Engineering Systems in Python (2020) not zbMATH
  5. 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)
  6. 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)
  7. Schweiger, G.; Nilsson, H.; Schoeggl, J.; Birk, W.; Posch, A.: Modeling and simulation of large-scale systems: a systematic comparison of modeling paradigms (2020)
  8. Pothen, Alex; Ferdous, S. M.; Manne, Fredrik: Approximation algorithms in combinatorial scientific computing (2019)
  9. Skjong, Stian; Pedersen, Eilif: On the numerical stability in dynamical distributed simulations (2019)
  10. Belmonte, Antonio; Garrido, Juan; Jiménez, Jorge E.; Vázquez, Francisco: Recomputing causality assignments on lumped process models when adding new simplification assumptions (2018)
  11. Jordan Jalving, Yankai Cao, Victor M. Zavala: Graph-Based Modeling and Simulation of Complex Systems (2018) arXiv
  12. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  13. Pytlak, Radosław; Suski, Damian; Tarnawski, Tomasz: Optimal control of hybrid systems with sliding modes (2018)
  14. Westman, Jonas; Nyberg, Mattias: Conditions of contracts for separating responsibilities in heterogeneous systems (2018)
  15. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  16. Chen, Mingshuai; Ravn, Anders P.; Wang, Shuling; Yang, Mengfei; Zhan, Naijun: A two-way path between formal and informal design of embedded systems (2017)
  17. Kohlhase, Michael; Koprucki, Thomas; Müller, Dennis; Tabelow, Karsten: Mathematical models as research data via flexiformal theory graphs (2017)
  18. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  19. Peleš, Slaven; Klus, Stefan: Sparse automatic differentiation for complex networks of differential-algebraic equations using abstract elementary algebra (2017)
  20. Minopoli, Stefano; Frehse, Goran: From simulation models to hybrid automata using urgency and relaxation (2016)

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