3 papers
physics.flu-dyn2026
G-PINNs: Gaussian-based spatially weighted formulation for PINNs: 1D low-viscous Burgers
Kheir-eddine Otmani, Abdelhalim Azzouz, Nourelhouda Groun +1
We introduce a Gaussian-based spatially weighted loss framework (G-PINNs) for physics-informed neural networks (PINNs) to improve the resolution of sharp discontinuities and shock…
eess.IV2024
EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach
Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin +4
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the field faces sign…
eess.IV2024
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification
Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin +4
In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to impr…