COMSOL

The COMSOL Multiphysics engineering simulation software environment facilitates all steps in the modeling process − defining your geometry, meshing, specifying your physics, solving, and then visualizing your results.Model set-up is quick, thanks to a number of predefined physics interfaces for applications ranging from fluid flow and heat transfer to structural mechanics and electromagnetic analyses. Material properties, source terms and boundary conditions can all be arbitrary functions of the dependent variables.Predefined multiphysics-application templates solve many common problem types. You also have the option of choosing different physics and defining the interdependencies yourself. Or you can specify your own partial differential equations (PDEs) and link them with other equations and physics.


References in zbMATH (referenced in 337 articles )

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  1. Acebrón, Juan A.: A probabilistic linear solver based on a multilevel Monte Carlo method (2020)
  2. Asghari, Hossein; Dardel, Morteza: Geometric and structural optimization of fluid energy harvester with high efficiency and bandwidth (2020)
  3. Du, Yu; Wu, Haijun; Zhang, Zhimin: Superconvergence analysis of linear FEM based on polynomial preserving recovery for Helmholtz equation with high wave number (2020)
  4. Hermanns, Miguel; Ibáñez, Santiago: Harmonic thermal response of thermally interacting geothermal boreholes (2020)
  5. Lukas Alber, Valentino Scalera, Vivek Unikandanunni, Daniel Schick, Stefano Bonetti: NTMpy: An open source package for solving coupled parabolic differential equations in the framework of the three-temperature model (2020) arXiv
  6. Michael Ortner; Lucas Gabriel; Coliado Bandeira: Magpylib: A free Python package for magnetic field computation (2020) not zbMATH
  7. Anshuman, Aatish; Eldho, T. I.; Singh, Laishram Guneshwor: Simulation of reactive transport in porous media using radial point collocation method (2019)
  8. Barac, Diana; Multerer, Michael D.; Iber, Dagmar: Global optimization using Gaussian processes to estimate biological parameters from image data (2019)
  9. Chen, Huatao; Guirao, Juan Luis García; Cao, Dengqing; Jiang, Jingfei; Fan, Xiaoming: Stochastic Euler-Bernoulli beam driven by additive white noise: global random attractors and global dynamics (2019)
  10. Dechaumphai, P.; Sucharitpwatsul, S.: Finite element analysis with COMSOL (2019)
  11. Du, Yu; Zhang, Zhimin: Supercloseness of linear DG-FEM and its superconvergence based on the polynomial preserving recovery for Helmholtz equation (2019)
  12. El Khatib, N.; Kafi, O.; Sequeira, A.; Simakov, S.; Vassilevski, Yu.; Volpert, V.: Mathematical modelling of atherosclerosis (2019)
  13. Gadeikytė, Aušra; Barauskas, Rimantas: Computer simulation of heat exchange through 3D fabric layer (2019)
  14. Guo, MingKai; Li, Yuan; Qin, GuoShuai; Zhao, MingHao: Nonlinear solutions of PN junctions of piezoelectric semiconductors (2019)
  15. Hermanns, Miguel; Ibáñez, Santiago: Thermal response of slender geothermal boreholes to subannual harmonic excitations (2019)
  16. Jankowska, Malgorzata A.; Karageorghis, Andreas: Variable shape parameter Kansa RBF method for the solution of nonlinear boundary value problems (2019)
  17. Jin, Yan; Chen, Kang Ping: Fundamental equations for primary fluid recovery from porous media (2019)
  18. Kafi, Oualid; Sequeira, Adélia: Mathematical modeling of inflammatory processes (2019)
  19. Karsenty, Avi; Mandelbaum, Yaakov: Computer algebra challenges in nanotechnology: accurate modeling of nanoscale electro-optic devices using finite elements method (2019)
  20. Kulvait, Vojtěch; Málek, Josef; Rajagopal, K. R.: The state of stress and strain adjacent to notches in a new class of nonlinear elastic bodies (2019)

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