From the 1 of 72 linked papers with an AI index.
1 citations · 1 across the 15 of their papers we have counts for
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Data-driven Discovery of Delay Differential Equations with Discrete Delays
Alessandro Pecile, Nicola Demo, Marco Tezzele +2
The Sparse Identification of Nonlinear Dynamics (SINDy) framework is a robust method for identifying governing equations, successfully applied to ordinary, partial, and stochastic…
Deep learning-based reduced-order methods for fast transient dynamics
Martina Cracco, Giovanni Stabile, Andrea Lario +5
In recent years, large-scale numerical simulations played an essential role in estimating the effects of explosion events in urban environments, for the purpose of ensuring the sec…
Stochastic Parameter Prediction in Cardiovascular Problems
Kabir Bakhshaei, Sajad Salavatidezfouli, Giovanni Stabile +1
Patient-specific modeling of cardiovascular flows with high-fidelity is challenging due to its dependence on accurately estimated velocity boundary profiles, which are essential fo…
Optimal Transport-Based Displacement Interpolation with Data Augmentation for Reduced Order Modeling of Nonlinear Dynamical Systems
Moaad Khamlich, Federico Pichi, Michele Girfoglio +2
We present a novel reduced-order Model (ROM) that leverages optimal transport (OT) theory and displacement interpolation to enhance the representation of nonlinear dynamics in comp…
A data-driven study on Implicit LES using a spectral difference method
Nicola Clinco, Niccolò Tonicello, Gianluigi Rozza
In this paper, we introduce a data-driven filter to analyze the relationship between Implicit Large-Eddy Simulations (ILES) and Direct Numerical Simulations (DNS) in the context of…
On the accuracy and efficiency of reduced order models: towards real-world applications
Pierfrancesco Siena, Paquale Claudio Africa, Michele Girfoglio +1
This chapter provides an extended overview about Reduced Order Models (ROMs), with a focus on their features in terms of efficiency and accuracy. In particular, the aim is to brows…