| KEYWORD |
Numerical methods for uncertainty quantification
keywords BAYESIAN INVERSE PROBLEMS, HIGH-DIMENSIONAL PARAMETRIC PDES, PDE-CONSTRAINED OPTIMIZATION, UNCERTAINTY QUANTIFICATION
Reference persons TOMMASO VANZAN
Thesis type BACHELOR THESIS, MASTER THESIS
Description In the last decades, there has been an increasing interest in improving the mathematical description of real-world systems by taking into account the often partial knowledge or the intrinsic stochasticity of the processes under study. Accordingly, this interest has paved the way to the development of rich mathematical theories and suitable numerical algorithms to ease the computational burden associated to the simulation of such models.
According to the student's interests and skills, this thesis proposal can focus on different aspects. To name a few: PDE-constrained optimization under uncertainty, Bayesian inverse problems, Pricing of financial instruments, Management of Smart grids, and approximation of high-dimensional parametric PDEs.
Deadline 05/05/2025
PROPONI LA TUA CANDIDATURA