Search Results for author:
Found 8 papers, 1 papers with code
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Heart diseases are the main international cause of human defunction.
Exploring the efficacy of a hybrid approach with modal decomposition over fully deep learning models for flow dynamics forecasting
Fluid dynamics problems are characterized by being multidimensional and nonlinear, causing the experiments and numerical simulations being complex, time-consuming and monetarily expensive.
A predictive physics-aware hybrid reduced order model for reacting flows
The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients.
Forecasting through deep learning and modal decomposition in two-phase concentric jets
We also show how NN training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic mode decomposition (HODMD), which identifies the main structures inside flow dynamics and reconstructs the original flow using only these main structures.
Improving aircraft performance using machine learning: a review
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring.
A Novel Data-Driven Method for the Analysis and Reconstruction of Cardiac Cine MRI
Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function.
Higher Order Dynamic Mode Decomposition: from Fluid Dynamics to Heart Disease Analysis
In this paper we apply HODMD, for the first time to the authors knowledge, for patterns recognition in echocardiography, specifically, echocardiography data taken from several mice, either in healthy conditions or afflicted by different cardiac diseases.
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow-field data useful for flow analysis, reduced-order modeling, and flow control.
