Fluid Dynamics Group · University of Nevada, Reno

Aditya G. Nair

Assistant Professor (Aerospace Program), Mechanical Engineering, University of Nevada, Reno

Modeling and controlling high-dimensional fluid flows through network science, reduced-order modeling, and scientific machine learning.

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About

Research at the intersection of fluids, data, and dynamics.

I lead the Fluid Dynamics Group at the University of Nevada, Reno, where we develop computational and data-driven frameworks for understanding, modeling, and controlling complex fluid flows. Our work blends computational fluid dynamics, network science, dynamical systems, and control theory to extract the essential interactions that govern high-dimensional flow physics.

My research is supported by a U.S. Department of Energy Early Career Award (Advanced Scientific Computing Research) on network-based simulation of coupled multi-physics systems and an Air Force Office of Scientific Research DEPSCoR award on estimation and control of aeroelastic flows, and I am affiliated with the NSF AI Institute for Dynamic Systems. Before joining UNR in 2020, I was a postdoctoral researcher at the University of Washington.

2018Ph.D., Mechanical Engineering — Florida State University
2018–20Postdoctoral Researcher — University of Washington, Seattle
2013M.S., Mechanical Engineering — University of Michigan, Ann Arbor
2011B.E., Mechanical Engineering — University of Mumbai
Research

What we work on.

01

Network-Theoretic Flow Modeling

Describing vortical and turbulent flows as networks of interacting elements — sparsified vortex dynamics, cluster-based network models, and networked-oscillator representations of coherent structures.

02

Reduced-Order Modeling & Scientific ML

Data-driven and physics-grounded reduced-order models: operator learning, latent dynamics, Koopman and spectral submanifold methods, and machine-learning surrogates for complex flow systems.

03

Flow Control, Aeroelasticity & FSI

Feedback and model-based control of separated flows, phase-amplitude reduction for oscillatory and aeroelastic dynamics, and multi-layer network approaches to fluid–structure interaction.

04

HPC for Coupled Multi-Physics

Graph dynamical systems and high-performance computing frameworks that model how coupled physical systems exchange information across scales.

Publications

Selected publications.

Selected recent work — see Google Scholar for the complete list.

2025Dominant balance-based adaptive mesh refinement for incompressible fluid flows. G. Kumar, A. G. Nair. Journal of Computational Physics, 114522.
2025Phase-based analysis and control of low-Reynolds-number aeroelastic flows. C. R. Sumanasiri, T. R. Sahu, A. G. Nair. Journal of Fluid Mechanics, 1020, R4.
2024Cluster regression model for flow control. N. Arya, A. G. Nair. Physics of Fluids, 36 (11).
2023Stabilizing two-dimensional turbulent Kolmogorov flow via selective modification of inviscid invariants. G. Kumar, A. G. Nair. Physics of Fluids, 35 (12).
2023Selective energy and enstrophy modification of two-dimensional decaying turbulence. A. G. Nair, J. Hanna, M. Aureli. Journal of Fluid Mechanics, 956, A12.
2023Data-driven unsteady aeroelastic modeling for control. M. K. Hickner, U. Fasel, A. G. Nair, B. W. Brunton, S. L. Brunton. AIAA Journal, 61 (2), 780–792.
2023Network-theoretic modeling of fluid–structure interactions. A. G. Nair, S. B. Douglass, N. Arya. Theoretical and Computational Fluid Dynamics.
2022Network-based analysis of fluid flows: progress and outlook. K. Taira, A. G. Nair. Progress in Aerospace Sciences, 131, 100823.
2021Phase-based control of periodic fluid flows. A. G. Nair, K. Taira, B. W. Brunton, S. L. Brunton. Journal of Fluid Mechanics, 927, A30.
2021Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization. M. Morimoto, K. Fukami, K. Zhang, A. G. Nair, K. Fukagata. Theoretical and Computational Fluid Dynamics.
Group

Fluid Dynamics Group.

Our group brings together students and collaborators working across computational fluid dynamics, scientific machine learning, and control.

Postdoctoral Researcher
Tulsi Ram Sahu
Ph.D. Students
Khalid Rafiq · Apoorva Parvathgari · Seyediman Mirafzal · Michael Stoddard
Master’s Student
Chathura Sumanasiri

Alumni

Graduated Students
Samuel Douglass · Brooks Karlik · Nathaniel Pavlik
Former Postdocs
Nitish Arya — Assistant Professor, IIT Roorkee
Gaurav Kumar — Research Scientist, A*STAR IHPC

I am always looking for motivated graduate students and postdocs interested in fluid mechanics, data-driven modeling, and control. Reach out by email.

Contact

Get in touch.

Email
adityan@unr.edu
Department
Mechanical Engineering
University of Nevada, Reno
Office
Reno, NV 89557
Palmer Engineering 232
© Aditya G. Nair · University of Nevada, Reno