collaborators

5 papers

eess.IV2026

Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI

Oliver Welin Odeback, Arivazhagan Geetha Balasubramanian, Jonas Schollenberger +9

4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its clinical application is limited by low s…

cond-mat.soft2026

Polyelectrolyte adsorption at the solid-liquid interface favors receding contact line instability

Léa Delance, Diego Díaz, Arivazhagan G. Balasubramanian +3

Controlling the motion of non-Newtonian drops on surfaces is crucial for applications ranging from inkjet printing to biomedical devices and food processing. While the macroscopic…

physics.flu-dyn2024

Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers

L. Guastoni, A. G. Balasubramanian, F. Foroozan +6

Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations…

physics.flu-dyn2024

Bursting bubble in an elasto-viscoplastic medium

A. G. Balasubramanian, V. Sanjay, M. Jalaal +2

A gas bubble sitting at a liquid-gas interface can burst following the rupture of the thin liquid film separating it from the ambient, owing to the large surface energy of the resu…

physics.flu-dyn2024

Prediction of flow and elastic stresses in a viscoelastic turbulent channel flow using convolutional neural networks

Arivazhagan G. Balasubramanian, Ricardo Vinuesa, Outi Tammisola

Neural-network models have been employed to predict the instantaneous flow close to the wall in a viscoelastic turbulent channel flow. Numerical simulation data at the wall is util…