Adaptive numerical algorithms for PDE problems with random inputs

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Partial differential equations (PDEs) are key tools in the mathematical modelling of processes in science and engineering. In practical PDE-based models, precise knowledge of inputs (e.g., material properties) may not be available, or there might be uncertainty about the inputs. In these cases the models are described by PDEs with random data. The project will address the development of adaptive algorithms for efficient numerical solution of PDEs with random inputs. The student will undertake the mathematical analysis of the developed algorithms and their implementation in a software package (using MATLAB).