106 citations · 883 across the 63 of their papers we have counts for
7 papers · 1 filter
Deep Learning for Stability Analysis of a Freely Vibrating Sphere at Moderate Reynolds Number
A. Chizfahm, R. Jaiman
In this paper, we present a deep learning-based reduced-order model (DL-ROM) for the stability prediction of unsteady 3D fluid-structure interaction systems. The proposed DL-ROM ha…
Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number
Rachit Gupta, Rajeev Jaiman
We present a deep learning-based reduced order model (DL-ROM) for predicting the fluid forces and unsteady vortex patterns. We consider flow past a sphere to examine the accuracy o…
An Interface-Driven Adaptive Variational Procedure for Fully Eulerian Fluid-Structure Interaction via Phase-field Modeling
Biswajeet Rath, Xiaoyu Mao, Rajeev K. Jaiman
In this paper, we present a novel interface-driven adaptive variational procedure using a fully Eulerian description of fluid-structure interaction. The proposed fully-Eulerian pro…
Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation
Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica
Although machine learning (ML) is increasingly employed recently for mechanistic problems, the black-box nature of conventional ML architectures lacks the physical knowledge to inf…
A high-fidelity numerical study on the propulsive performance of pitching flexible plates
Guojun Li, Gael Kemp, Rajeev Kumar Jaiman +1
In this paper, we numerically investigate the propulsive performance of three-dimensional pitching flexible plates with varying flexibility and trailing edge shapes. To eliminate t…
A robust and accurate finite element framework for cavitating flows with fluid-structure interaction
Suraj R. Kashyap, Rajeev K. Jaiman
We present a unified variational mechanics framework for cavitating turbulent flows and structural motions via a stabilized finite element formulation. To model the finite mass tra…
A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction
Rachit Gupta, Rajeev Kumar Jaiman
We present a hybrid partitioned deep learning framework for the reduced-order modeling of fluid-structure interaction. Using the discretized Navier-Stokes in the arbitrary Lagrangi…