Computational Mathematics

Computational mathematics has become indispensable in modern society, serving as the bridge between theoretical science and real-world applications. As complex problems in engineering, physics, biology, and finance cannot be solved analytically, computational methods provide the tools necessary to extract insights and drive innovation. Today, computational methods are used to gain insight in almost all areas of mathematics, from number theory, geometry, and analysis to statistics, optimization, and mathematical physics.

Among the interests of the research group are numerical methods for solving multiscale problems, nonlinear filtering of stochastic differential equations, numerical methods for random fields, numerical analysis of deterministic and stochastic PDEs, geometric numerical integration, machine learning, techniques from harmonic analysis to numerically solve elliptic PDEs, computational methods in general relativity, inverse problems, modelling in mathematical biology, linear and non-linear eigenvalue problems, matrix equations, and low-rank structures, computational algebraic number theory, matrix hydrodynamics, stochastic processes for comparative genomics, large-scale simulation and data-driven modelling for urban digital twins, and computational methods and software for the design of materials.