Zobrazeno 1 - 10
of 1 279
pro vyhledávání: '"Regazzoni, P"'
The left ventricular end-systolic pressure-volume relationship (ESPVr) is a key indicator of cardiac contractility. Despite its established importance, several studies suggested that the mechanical mode of contraction, such as isovolumetric or ejecti
Externí odkaz:
http://arxiv.org/abs/2411.06115
In this work, we aim to formalize a novel scientific machine learning framework to reconstruct the hidden dynamics of the transmission rate, whose inaccurate extrapolation can significantly impair the quality of the epidemic forecasts, by incorporati
Externí odkaz:
http://arxiv.org/abs/2410.11545
We propose a non-intrusive method to build surrogate models that approximate the solution of parameterized partial differential equations (PDEs), capable of taking into account the dependence of the solution on the shape of the computational domain.
Externí odkaz:
http://arxiv.org/abs/2409.12400
Security engineering, from security requirements engineering to the implementation of cryptographic protocols, is often supported by domain-specific languages (DSLs). Unfortunately, a lack of knowledge about these DSLs, such as which security aspects
Externí odkaz:
http://arxiv.org/abs/2408.06219
Two new calibration techniques of lumped-parameter mathematical models for the cardiovascular system
Autor:
Tonini, Andrea, Regazzoni, Francesco, Salvador, Matteo, Dede', Luca, Scrofani, Roberto, Fusini, Laura, Cogliati, Chiara, Pontone, Gianluca, Vergara, Christian, Quarteroni, Alfio
Cardiocirculatory mathematical models serve as valuable tools for investigating physiological and pathological conditions of the circulatory system. To investigate the clinical condition of an individual, cardiocirculatory models need to be personali
Externí odkaz:
http://arxiv.org/abs/2405.11915
This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world
Externí odkaz:
http://arxiv.org/abs/2403.13827
Multiphysics simulations frequently require transferring solution fields between subproblems with non-matching spatial discretizations, typically using interpolation techniques. Standard methods are usually based on measuring the closeness between po
Externí odkaz:
http://arxiv.org/abs/2403.03665
The following paper proposes a novel Vehicle-to-Everything (V2X) network abnormality detection scheme based on Bayesian generative models for enhanced network self-awareness functionality at the Base station (BS). In the learning phase, multi-modal d
Externí odkaz:
http://arxiv.org/abs/2403.03583
Autor:
Pilato, Christian, Banik, Subhadeep, Beranek, Jakub, Brocheton, Fabien, Castrillon, Jeronimo, Cevasco, Riccardo, Cmar, Radim, Curzel, Serena, Ferrandi, Fabrizio, Friebel, Karl F. A., Galizia, Antonella, Grasso, Matteo, Silva, Paulo, Martinovic, Jan, Palermo, Gianluca, Paolino, Michele, Parodi, Andrea, Parodi, Antonio, Pintus, Fabio, Polig, Raphael, Poulet, David, Regazzoni, Francesco, Ringlein, Burkhard, Rocco, Roberto, Slaninova, Katerina, Slooff, Tom, Soldavini, Stephanie, Suchert, Felix, Tibaldi, Mattia, Weiss, Beat, Hagleitner, Christoph
Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly e
Externí odkaz:
http://arxiv.org/abs/2402.12612
Autor:
Zappon, Elena, Salvador, Matteo, Piersanti, Roberto, Regazzoni, Francesco, Dede', Luca, Quarteroni, Alfio
When generating in-silico clinical electrophysiological outputs, such as electrocardiograms (ECGs) and body surface potential maps (BSPMs), mathematical models have relied on single physics, i.e. of the cardiac electrophysiology (EP), neglecting the
Externí odkaz:
http://arxiv.org/abs/2402.06308