106 citations · 883 across the 63 of their papers we have counts for
10 papers · 1 filter
A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
An emerging trend in deep learning research focuses on the applications of graph neural networks (GNNs) for mesh-based continuum mechanics simulations. Most of these learning frame…
Unsteady aeroelastic characterization and scaling relations of flexible membrane wings
Guojun Li, Rajeev Kumar Jaiman
We present a numerical study to characterize nonlinear unsteady aeroelastic interactions of two-dimensional flexible wings at high angles of attack. The coupled fluid-flexible wing…
Self-sustained oscillations in whiskers without vortex shedding
Shayan Heydari, Mitra J. Z. Hartmann, Neelesh A. Patankar +1
Sensing the flow of water or air disturbance is critical for the survival of many animals: flow information helps them localize food, mates, and prey and to escape predators. Acros…
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics
Indu Kant Deo, Rui Gao, Rajeev Jaiman
There is a critical need for efficient and reliable active flow control strategies to reduce drag and noise in aerospace and marine engineering applications. While traditional full…
Predicting fluid-structure interaction with graph neural networks
Rui Gao, Rajeev K. Jaiman
We present a rotation equivariant, quasi-monolithic graph neural network framework for the reduced-order modeling of fluid-structure interaction systems. With the aid of an arbitra…
Assessment of convolutional recurrent autoencoder network for learning wave propagation
Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica
It is challenging to construct generalized physical models of wave propagation in nature owing to their complex physics as well as widely varying environmental parameters and dynam…
Unsteady cavitation dynamics and frequency lock-in of a freely vibrating hydrofoil at high Reynolds number
Suraj R. Kashyap, Rajeev K. Jaiman
We investigate the influence of unsteady partial cavitation on the fluid-structure interaction of a freely vibrating hydrofoil section at high Reynolds numbers. We consider an elas…
Deep convolutional neural network for shape optimization using level-set approach
Wrik Mallik, Neil Farvolden, Jasmin Jelovica +1
This article presents a reduced-order modeling methodology via deep convolutional neural networks (CNNs) for shape optimization applications. The CNN provides a nonlinear mapping b…
An interface and geometry preserving phase-field method for fully Eulerian fluid-structure interaction
Xiaoyu Mao, Rajeev Jaiman
We present an interface and geometry preserving (IGP) method for the modeling of fully Eulerian fluid-structure interaction via phase-field formulation. While the hyperbolic tangen…
Predicting waves in fluids with deep neural network
Indu Kant Deo, Rajeev Jaiman
In this paper, we present a deep learning technique for data-driven predictions of wave propagation in a fluid medium. The technique relies on an attention-based convolutional recu…