Modelica

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 87 articles )

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  1. Di Pietro, Franco; Fernández, Joaquín; Migoni, Gustavo; Kofman, Ernesto: Mixed-mode state-time discretization in ODE numerical integration (2020)
  2. Francesco Witte; Ilja Tuschy: TESPy: Thermal Engineering Systems in Python (2020) not zbMATH
  3. Schweiger, G.; Nilsson, H.; Schoeggl, J.; Birk, W.; Posch, A.: Modeling and simulation of large-scale systems: a systematic comparison of modeling paradigms (2020)
  4. Pothen, Alex; Ferdous, S. M.; Manne, Fredrik: Approximation algorithms in combinatorial scientific computing (2019)
  5. Belmonte, Antonio; Garrido, Juan; Jiménez, Jorge E.; Vázquez, Francisco: Recomputing causality assignments on lumped process models when adding new simplification assumptions (2018)
  6. Jordan Jalving, Yankai Cao, Victor M. Zavala: Graph-Based Modeling and Simulation of Complex Systems (2018) arXiv
  7. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  8. Pytlak, Radosław; Suski, Damian; Tarnawski, Tomasz: Optimal control of hybrid systems with sliding modes (2018)
  9. Westman, Jonas; Nyberg, Mattias: Conditions of contracts for separating responsibilities in heterogeneous systems (2018)
  10. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  11. Chen, Mingshuai; Ravn, Anders P.; Wang, Shuling; Yang, Mengfei; Zhan, Naijun: A two-way path between formal and informal design of embedded systems (2017)
  12. Kohlhase, Michael; Koprucki, Thomas; Müller, Dennis; Tabelow, Karsten: Mathematical models as research data via flexiformal theory graphs (2017)
  13. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  14. Peleš, Slaven; Klus, Stefan: Sparse automatic differentiation for complex networks of differential-algebraic equations using abstract elementary algebra (2017)
  15. Minopoli, Stefano; Frehse, Goran: From simulation models to hybrid automata using urgency and relaxation (2016)
  16. Scholz, Lena; Steinbrecher, Andreas: Regularization of DAEs based on the signature method (2016)
  17. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015) not zbMATH
  18. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  19. Hannemann-Tamás, Ralf; Muñoz, Diego A.; Marquardt, Wolfgang: Adjoint sensitivity analysis for nonsmooth differential-algebraic equation systems (2015)
  20. Zhu, Longfei; Xu, Qiwen; He, Jifeng; Zhu, Huibiao: A formal model for a hybrid programming language (2015)

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