RESEARCH GROUPS

Area Architecture

Area Engineering

DEPARTMENTS

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