3 citations · 6 across the 4 of their papers we have counts for
Showing physics.flu-dynShow all
2 papers · 1 filter
physics.flu-dyn2024★ 1 cited
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…
physics.flu-dyn2023
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…