Alexander Litvinenko

Previous Members

Senior Research Scientist​​​​

Research Interests

Very often mathematical models  (given by partial or ordinary differential equations) contain parameters which are uncertain. Typical examples are conductivity coefficients in groundwater flow problems and porosity. The uncertain heterogeneity in the material can affect the system behaviour dramatically. One of the mostly used techniques to model such uncertainties is random fields. To solve resulting PDE with random fields (stochastic PDE) numerically one has to discretise the deterministic operator as well as the high-dimensional stochastic operator. There are different methods to discretise and to solve these stochastic PDEs: stochastic Galerkin, collocation, sparse grids, (quasi) MC etc.
To reduce the computational complexity the stochastic forward problem is approximated in a low-rank/sparse tensor data format.


2007-2013, PostDoc at Institute of Scientific Computing,TU Braunschweig, Germany,
2002-2006, Promotion in the group of Scientific computing at Max-Planck-Institut fuer Mathematik in den Naturwissenschaften, Leipzig, Germany,
2000-2002, Master degree in Mathematics at Novosibirsk State University and Laboratory of Data Analysis of Sobolev Institute of Mathematics (Russian Academy of Sciences)
1996-2000, Bachelor degree in Mathematics at Novosibirsk State University

Scientific and Professional Memberships


KAUST Affiliations

  • Computer, Electrical and Mathematical Sciences & Engineering Division.
  • Member of KAUST ECRC and SRI Center for Uncertainty Quantification in Computational Science and Engineering: