Computational Mathematics

We teach and perform research on the design, analysis, implementation and application of numerical methods for the solution of ordinary and partial differential equations. In particular, we study adaptive finite element methods, stochastic partial differential equations, transport equations, geometric integration and applications in medical image registration, inverse problems, mathematical physics, design, architecture, construction and virtual/augmented reality (VR/AR).  We also work on deep learning for solving partial differential equations. 

We are active within the Chalmers Areas of Advance, in particular within Digital Twin Cities and Information and Communication Technologies.


We organize the weekly Computational and Applied Mathematics Seminar (CAM seminar).  The seminar meets on Wednesdays at 14.15-15.00 in room MVL14.

Researchers and teachers

Mohammad Asadzadeh ​Finite element methods, partial differential equations
​Joakim Becker ​Finite element methods for partial differential equations
Larisa Beilina ​Inverse problems, adaptive finite element methods, high-performance scientific computing, real-life applications
Katarina Blom ​Linear algebra
​David Cohen ​Numerical analysis of (stochastic) differential equations, especially geometric numerical integration
Annika Lang ​Stochastic partial differential equations, random fields, stochastic simulation
​Stig Larsson Finite element methods, deterministic and stochastic PDE ​
Anders Logg ​Finite element methods, adaptivity, high-performance computing, applications ​
Klas Modin ​Geometric numerical integration, shape analysis, geometric hydrodynamics
​Axel Målqvist ​Partial differential equations, multiscale problems
​Irina Pettersson ​Asymptotic analysis and homogenization theory
Ivar Gustafsson ​Iterative methods for linear systems, parallel computations
​Göran Starius ​Differential equations
Vidar Thomée ​Numerical analysis of evolution problems
​​Postdocs and researchers
Adam Andersson ​Deep learning
​Fredrik Hellman ​Multiscale problems
​Vasilis Naserentin ​Digital twin cities
Mike Pereira ​Deep learning for traffic flow problems
​​PhD students
​Morgan Görtz ​Industrial PhD student at FCC.  Multiscale methods for paper forming
​Per Ljung ​FEM for multiscale problems
Carl Lundholm ​Analysis and applications of finite element methods on overlapping meshes ​
Malin Nilsson ​Multiscale problems
Andreas Petersson ​Stochastic partial differential equations
​Milo Viviani ​Geometric integration
Filip Wikman ​Deep learning for stochastic partial differential equations



FEniCS logotypeFEniCS is a world-leading computing platform for the solution of partial differential equations in Python and C++. FEniCS was founded in collaboration between Chalmers and the University of Chicago in 2003 and has since reached widespread use and recognition worldwide.


Published: Tue 07 Apr 2020.