Adaptive numerical algorithms for PDE problems with random inputs

About the project

About the Project PhD Studentship in Numerical Analysis and Scientific Computing The project concerns the numerical solution of partial differential equations (PDEs) with uncertain inputs (e.g., material properties, external forces, geometry of the computational domain). It will focus on developing adaptive algorithms for efficient solution of such problems. Of particular interest are effective algorithms for domain uncertainty quantification —computing PDE solutions on domains with uncertain geometric features (e.g., size, shape or uncertain boundary features due to manufacturing defects). The work on the project will combine rigorous mathematical analysis, the development and implementation of numerical algorithms as well as extensive numerical experimentation. The project will provide training in state-of-the-art numerical analysis and uncertainty quantification techniques, equipping the student with sought-after skills for careers in industry or academia. Entry requirements: We are looking for a highly enthusiastic and motivated student with - a strong 1st class degree in Mathematics at the MMath/MSci/MSc level, or equivalent; - a solid background in numerical analysis of PDEs; - excellent programming skills; - excellent communication skills (oral and written). Informal inquiries should be directed to Dr Alex Bespalov, e-mail: a.bespalov@bham.ac.uk

Research areas