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
||Finite element methods for partial differential equations|
||Inverse problems, adaptive finite element methods, high-performance scientific computing, real-life applications|
||Numerical analysis of (stochastic) differential equations, especially geometric numerical integration|
||Stochastic partial differential equations, random fields, stochastic simulation|
||Finite element methods, deterministic and stochastic PDE |
||Finite element methods, adaptivity, high-performance computing, applications |
||Geometric numerical integration, shape analysis, geometric hydrodynamics|
||Partial differential equations, multiscale problems|
||Asymptotic analysis and homogenization theory|
||Computational optimal transport|
|Adjunct and affiliated faculty
||Stochastic and fractional PDE|
||Finite element methods, partial differential equations|
||Iterative methods for linear systems, parallel computations|
||Numerical analysis of evolution problems|
|Postdocs and researchers
||Digital twin cities|
||Industrial PhD student at FCC. Multiscale methods for paper forming|
||Shape analysis and deep learning|
||Inverse problems for Maxwell's equations|
||FEM for multiscale problems|
||Machine learning, Monte Carlo methods for Bayesian inverse problems|
||Approximation of random fields|
||Geometric numerical hydrodynamics
||Numerical methods for SPDE
FEniCS 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.