Numerical methods for high-dimensional data

ABOUT THE SCHOOL

The increasing complexity of modern scientific and engineering problems requires advanced numerical methods capable of handling high-dimensional data efficiently. This school will introduce participants to state-of-the-art techniques in numerical analysis and scientific computing, focusing on challenges arising in high-dimensional settings.

Topics will include model reduction techniques, uncertainty quantification, optimization, and machine learning approaches for solving high-dimensional partial differential equations. The lectures, delivered by leading experts such as Bruno Després, Michael Herty, and George Karniadakis, will provide both theoretical insights and practical applications.

The school is designed for PhD students, postdoctoral researchers, and advanced graduate students interested in high-dimensional computational mathematics. Alongside traditional lectures, hands-on sessions will offer participants the opportunity to implement and test numerical methods on relevant problems.

The school is free of charge but the registration is mandatory before the 25th of July 2025 at the Registration page.

The event is organized within the activities of thematic semester on "Data Science", as part of the Excellence Department initiative (2023-2027) in the area of Mathematics and Computer Science, funded by the Italian National Agency for the Evaluation of Universities and Research Institutes.

A workshop on "Optimal Control and Agent Systems" is organized the week after the school (September 22-25, 2025).

ORGANIZERS

Alessandro Alla · Davide Torlo · Giuseppe Visconti

SPONSORS

Department of Mathematics within Excellence Departments 2023-2027