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, multiscale problems and applications in medical image registration, inverse problems, mathematical physics, design, architecture, construction, fibre networks, composite materials 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 13.15-14.00 in room MV:L14.

Researchers and teachers

​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
Axel Ringh ​Computational optimal transport
Adjunct and affiliated faculty
​Adam Andersson​ ​Deep learning
Mihály Kovács ​Stochastic and fractional PDE
Mohammad Asadzadeh ​Finite element methods, partial differential equations
Ivar Gustafsson ​Iterative methods for linear systems, parallel computations
​Göran Starius ​Differential equations
Vidar Thomée ​Numerical analysis of evolution problems
​​Postdocs and researchers
​Fredrik Hellman ​Multiscale problems
​Vasilis Naserentin ​Digital twin cities
​​PhD students
​Kasper Bågmark ​Machine learning
​Morgan Görtz ​Industrial PhD student at FCC.  Multiscale methods for paper forming
​Erik Jansson ​Shape analysis and deep learning
Eric Lindström​ ​Inverse problems for Maxwell's equations
​Per Ljung ​FEM for multiscale problems
Vincent Molin Machine learning, Monte Carlo methods for Bayesian inverse problems​
Ioanna Motschan-Armen ​Approximation of random fields
Malin Nilsson ​Multiscale problems
Michael Roop ​Geometric numerical hydrodynamics
Johan Ulander ​Numerical methods for SPDE



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.


Page manager Published: Mon 10 Oct 2022.