Bhargav Sriram Siddani
Postdoctoral Scholar, Applied Mathematics and Computational Research Division
I am a postdoctoral scholar in the
Center for Computational
Sciences and Engineering (CCSE) in the
Computing Sciences Area
at the Lawrence Berkeley National Laboratory.
My research background is in particle-laden multiphase flows and data-driven modeling for these flow applications.
I am currently involved in the development of multiscale framework for
Complex Fluids and Multiphase Flow and
data-driven model development and integration into this framework.
My research interests include computational multiphase flows, scientific machine
learning for fluid mechanics, and high performance scientific computing.
I obtained my Ph.D.(2022) and M.S.(2019) in Mechanical Engineering from the University of Florida, and B.Tech.(2017)
in Mechanical Engineering from National Institute of Technology Karnataka, India. Upon graduation I worked as a
postdoctoral scholar(2023) with the NETL MFS group.
Recent Publications
- Siddani, B., Balachandar, S., Zhou, J., & Subramaniam, S. (2024).
Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning.
AIChE Journal, e18339.
- Siddani, B., & Balachandar, S. (2023).
Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability.
Physical Review Fluids, 8(1), 014303.
- Siddani, B., Balachandar, S., Moore, W. C., Yang, Y., & Fang, R. (2021).
Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks.
Theoretical and Computational Fluid Dynamics, 35, 807-830.
- Siddani, B., Balachandar, S., & Fang, R. (2021).
Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration.
Physics of Fluids, 33(10).
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