activity
20222026
most citedPredicting the wall-shear stress and wall pressure through convolutional neural networks

13 citations · 28 across the 6 of their papers we have counts for

collaborators

6 papers

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…

eess.IV2025

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…

physics.flu-dyn2024★ 12 cited

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★ 2 cited

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…

physics.flu-dyn2023★ 13 cited

Predicting the wall-shear stress and wall pressure through convolutional neural networks

Arivazhagan G. Balasubramanian, Luca Guastoni, Philipp Schlatter +2

The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are perfor…

physics.flu-dyn2022★ 1 cited

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…