11 citations · 23 across the 5 of their papers we have counts for
5 papers
Reconstruction of three-dimensional turbulent flows from sparse and noisy planar measurements: A weight-sharing neural network approach
Yaxin Mo, Luca Magri
This paper proposes a method for reconstructing three-dimensional turbulent flows from sparse measurements without the need for ground truth data during training. A weight-sharing…
Inferring stability properties of chaotic systems on autoencoders' latent spaces
Elise Özalp, Luca Magri
The data-driven learning of solutions of partial differential equations can be based on a divide-and-conquer strategy. First, the high dimensional data is compressed to a latent sp…
Stability analysis of chaotic systems in latent spaces
Elise Özalp, Luca Magri
Partial differential equations, and their chaotic solutions, are pervasive in the modelling of complex systems in engineering, science, and beyond. Data-driven methods can find sol…
Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network
Yaxin Mo, Luca Magri
Data from fluid flow measurements are typically sparse, noisy, and heterogeneous, often from mixed pressure and velocity measurements, resulting in incomplete datasets. In this pap…
Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Data
Yaxin Mo, Tullio Traverso, Luca Magri
Turbulent flows are chaotic and multi-scale dynamical systems, which have large numbers of degrees of freedom. Turbulent flows, however, can be modelled with a smaller number of de…