A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization
We present a numerical method to efficiently solve optimization problems governed by large-
scale nonlinear systems of equations, including discretized partial differential equations …
scale nonlinear systems of equations, including discretized partial differential equations …
Deep convolutional neural network for shape optimization using level-set approach
This article presents a reduced-order modeling methodology via deep convolutional neural
networks (CNNs) for shape optimization applications. The CNN provides a nonlinear …
networks (CNNs) for shape optimization applications. The CNN provides a nonlinear …
A parametric level set method with convolutional encoder-decoder network for shape optimization with fluid flow
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 …
hydrofoil morphing via a polynomial perturbation of parametric level set representation …
Efficient Design of Transonic Airfoils Using Non-Intrusive Reduced Order Models and Composite Bayesian Optimization
A Dikshit, LT Leifsson - AIAA AVIATION FORUM AND ASCEND 2024, 2024 - arc.aiaa.org
Non-intrusive reduced order models (ROMs) are becoming increasingly popular in the
prediction of aerodynamic flow fields and surface pressure distributions. However, the use of …
prediction of aerodynamic flow fields and surface pressure distributions. However, the use of …
Adaptive Model Hyperreduction to Accelerate Optimization Problems Governed by Partial Differential Equations
T Wen - 2024 - search.proquest.com
Optimization problems constrained by partial differential equations (PDEs) are prevalent
across modern science and engineering. They are crucial in the optimal design and control …
across modern science and engineering. They are crucial in the optimal design and control …