49 citations · 94 across the 6 of their papers we have counts for
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physics.flu-dyn2022★ 43 cited
Point-particle drag, lift, and torque closure models using machine learning: hierarchical approach and interpretability
B. Siddani, S. Balachandar
Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest in recent times from dispersed multiphase flow community.…
physics.flu-dyn2021★ 1 cited
Rotational and Reflectional Equivariant Convolutional Neural Network for data-limited applications: Multiphase Flow demonstration
Bhargav Sriram Siddani, S. Balachandar, Ruogu Fang
This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles…
physics.flu-dyn2020★ 49 cited
Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks
B. Siddani, S. Balachandar, W. C. Moore +2
Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we presen…