49 citations · 94 across the 6 of their papers we have counts for
6 papers
Capturing non-Markovian dynamics in non-equilibrium stochastic systems using flow matching
Bhargav Sriram Siddani, John B. Bell, Alejandro L. Garcia +1
Hydrodynamic models of stochastic particle systems represented by coarse-grained stochastic partial differential equations (SPDE), such as the regularized Dean-Kawasaki (DK) equati…
Recent Modeling Advances in Dense Suspension Rheology
Orhun Ayar, Bhargav Sriram Siddani, Ishan Srivastava +1
Dense suspensions of particles dispersed in liquids are central to industrial and geophysical processes and serve as model systems for out-of-equilibrium soft matter. At high parti…
An adaptive, data-driven multiscale approach for dense granular flows
B. Siddani, Weiqun Zhang, Andrew Nonaka +2
The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing thi…
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.…
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…
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…