iNEST Spoke 9 presents advanced modeling methods at ICOSAHOM 2025
The participation in the Canadian conference is funded through two SISSA young researcher grants
Montréal, 13th July 2025
ICOSAHOM 2025, the 15th edition of the congress on spectral and high-order methods for the solution of differential equations, opens today at McGill University in Montréal, Canada. iNEST Spoke 9 will be represented at the conference through the minisymposium MS129 – Accelerating High-Order CFD Simulations with Surrogate Modeling, featuring contributions by researchers Pasquale Claudio Africa and Niccolò Tonicello, whose participation is supported by a Young Researcher Grant, as well as Federico Pichi and the Spoke coordinator, Prof. Gianluigi Rozza.
Established in 1989, ICOSAHOM is one of the leading international events for experts working on advanced mathematical tools for solving differential equations—laws that describe how quantities evolve over time or space, and that are fundamental to dynamic modeling.
The Spoke 9 minisymposium will focus on the use of surrogate models in computational fluid dynamics: these are approximate mathematical models that allow the estimation of quantities that cannot be directly computed or measured. The session aims to foster dialogue between academic research and industrial applications, with potential impacts in fields such as aerospace engineering and biomedicine, where surrogate models are widely used to simulate airflow and blood flow.
Africa and Tonicello will also be featured in other minisymposia: the former, in minisymposium MS124—on Monday, 14th July—with Multigrid Methods with Polytopic Agglomeration for Discontinuous Galerkin Modeling in Cardiac Electrophysiology; the latter, in minisymposium MS148—on Thursday, 17th July—with Fully-discrete spatial eigenanalysis of discontinuous spectral element methods. Also on Monday, at 4:30 PM, Prof. Rozza will speak during the minisymposium MS140, held in honor of Anthony Patera, with a talk titled Enhancing CFD Simulations for Digital Twins by Surrogate Model Order Reduction with Scientific Machine Learning.