Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling

no code implementations • 25 May 2022 • Shady E. Ahmed, Omer San, Adil Rasheed, Traian Iliescu, Alessandro Veneziani

We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost.

BIG-bench Machine Learning

Modeling of Asymptotically Periodic Outbreaks: a long-term SIRW2 description of COVID-19?

no code implementations • 15 Mar 2022 • Alex Viguerie, Margherita Carletti, Alessandro Veneziani, Guido Silvestri

As the outbreak of COVID-19 enters its third year, we have now enough data to analyse the behavior of the pandemic with mathematical models over a long period of time.

Patient-Specific 3D Volumetric Reconstruction of Bioresorbable Stents: A Method to Generate 3D Geometries for Computational Analysis of Coronaries Treated with Bioresorbable Stents

no code implementations • 8 Oct 2018 • Boyi Yang, Marina Piccinelli, Gaetano Esposito, Tianli Han, Yasir Bouchi, Bill Gogas, Don Giddens, Habib Samady, Alessandro Veneziani

This work is the first step toward a fully automated reconstruction and simulation procedure aiming at an extensive quantitative analysis of the impact of BVS struts on hemodynamics via CFD in clinical trials, going beyond the proof-of-concept stage.

3D Volumetric Reconstruction