From the 1 of 8 linked papers with an AI index.
8 papers
Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics
Irena RadiÅ¡iÄ, Raffaele Tirotta, Alberto Zingaro +2
The paper introduces a physics-informed neural network (PINN) framework that combines incompressible Navier‑Stokes equations with sparse experimental velocity data to reconstruct h…
K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles
Giulia Patanè, Alessandra Menafoglio, Alexander Krauth +4
Existing clustering methods for functional data often prioritize partitioning accuracy over interpretability, making it challenging to extract meaningful insights when the data-gen…
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems
Andrea Tonini, Luca Dede'
Among other uses, neural networks are a powerful tool for solving deterministic and Bayesian inverse problems in real-time, where variational autoencoders, a specialized type of ne…
Improvements on uncertainty quantification with variational autoencoders
Andrea Tonini, Tan Bui-Thanh, Francesco Regazzoni +2
Inverse problems aim to determine model parameters of a mathematical problem from given observational data. Neural networks can provide an efficient tool to solve these problems. I…
A reduced 3D-0D FSI model of the aortic valve including leaflet curvature
Ivan Fumagalli, Luca Dede', Alfio Quarteroni
We introduce an innovative lumped-parameter model of the aortic valve, designed to efficiently simulate the impact of valve dynamics on blood flow. Our reduced model includes the e…
Space--time Isogeometric Analysis of cardiac electrophysiology
P. F. Antonietti, L. Dedè, G. Loli +3
This work proposes a stabilized space--time method for the monodomain equation coupled with the Rogers--McCulloch ionic model, which is widely used to simulate electrophysiological…