3 citations · 6 across the 5 of their papers we have counts for
5 papers
A Graph Neural Network Surrogate Model for Multi-Objective Fluid-Acoustic Shape Optimization
Farnoosh Hadizadeh, Wrik Mallik, Rajeev K. Jaiman
This article presents a graph neural network (GNN) based surrogate modeling approach for fluid-acoustic shape optimization. The GNN model transforms mesh-based simulations into a c…
A parametric level set method with convolutional encoder-decoder network for shape optimization with fluid flow
Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica
In this article, we present a new data-driven shape optimization approach for implicit hydrofoil morphing via a polynomial perturbation of parametric level set representation. With…
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…
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…
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…