Area of research
Computational Mechanics · Statistical and Nonlinear Physics
Research interest
Research interests include Fluid Dynamics and Turbulent Flows, Model Reduction and Neural Networks, Aerodynamics and Acoustics in Jet Flows, and Fluid Dynamics and Vibration Analysis.
Observable-augmented manifold learning for multi-source turbulent flow data
Machine learning in fluid dynamics: A critical assessment
Single-snapshot machine learning for super-resolution of turbulence
A cyclic perspective on transient gust encounters through the lens of persistent homology
Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables
Data-driven transient lift attenuation for extreme vortex gust–airfoil interactions
Aerodynamics-guided machine learning for design optimization of electric vehicles
Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks
Data-Driven Modeling, Sensing, and Control of Extreme Vortex-Airfoil Interactions
Super-resolution analysis via machine learning: a survey for fluid flows
Grasping extreme aerodynamics on a low-dimensional manifold
Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization
Experimental velocity data estimation for imperfect particle images using machine learning
Assessment of supervised machine learning methods for fluid flows
Model order reduction with neural networks: Application to laminar and turbulent flows
Super-resolution reconstruction of turbulent flows with machine learning
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
Super-resolution analysis with machine learning for low-resolution flow data
2019cited by 12position: first