Project on uncertainty quantification of Hamiltonian maps using intrusive polynomial chaos expansion,
Intrusive Polynomial Chaos methods are used to solve stochastic PDEs efficiently. With the right conditions, they can significantly outperform traditional Monte Carlo-based methods. In this thesis we study efficient tracking of uncertainties in systems modelled by stochastic equations in Hamiltonian mechanics, such as particle accelerators.
This repository contains the final version delivered for my ETH Zurich Master of Computational Sciences and Engineering thesis.