6 citations · 9 across the 6 of their papers we have counts for
6 papers
Information decomposition for disentangled and interpretable manifold learning of fluid flows via variational autoencoders
Zhiyuan Wang, Iacopo Tirelli, Stefano Discetti +1
We introduce an information-theoretic framework that uses variational autoencoders (VAEs) to extract compact, physically interpretable manifolds from high-dimensional flow-field da…
Meshless Super-Resolution of Scattered Data via constrained RBFs and KNN-Driven Densification
Iacopo Tirelli, Miguel Alfonso Mendez, Andrea Ianiro +1
We propose a novel meshless method to achieve super resolution from scattered data obtained from sparse, randomly positioned sensors such as the particle tracers of particle tracki…
Full-domain POD modes from PIV asynchronous patches
Iacopo Tirelli, Adrian Grille Guerra, Andrea Ianiro +3
A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (ove…
A meshless method to compute the proper orthogonal decomposition and its variants from scattered data
Iacopo Tirelli, Miguel Alfonso Mendez, Andrea Ianiro +1
Complex phenomena can be better understood when broken down into a limited number of simpler "components". Linear statistical methods such as the principal component analysis and i…
A simple trick to improve the accuracy of PIV/PTV data
Iacopo Tirelli, Andrea Ianiro, Stefano Discetti
Particle Image Velocimetry (PIV) estimates velocities through correlations of particle images within interrogation windows, leading to a spatial modulation of the velocity field. A…
An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification
Iacopo Tirelli, Andrea Ianiro, Stefano Discetti
We introduce a novel end-to-end approach to improving the resolution of PIV measurements. The method blends information from different snapshots without the need for time-resolved…