Our project is inspired by the increasing development and interest in scientific machine learning applied to multi-fidelity data assimilation and inverse problems.  Methods to approximate probability measures have flourished recently also due to the implementation of efficient generative models in machine learning and more principled mathematical methods like diffusion models and transport maps.

KEYWORDS: machine learning, generative models, data assimilation, inverse problems

PROJECT DURATION: 2 years

PARTNER: SISSA MathLab, MIT

PEOPLE INVOLVED: Marco Tezzele, Francesco Romor, Gianluigi Rozza