1 citations · 1 across the 2 of their papers we have counts for
2 papers
physics.flu-dyn2024
Nonlinear Reduced-Order Modeling of Compressible Flow Fields Using Deep Learning and Manifold Learning
Bilal Mufti, Christian Perron, Dimitri N. Mavris
This paper presents a nonlinear reduced-order modeling (ROM) framework that leverages deep learning and manifold learning to predict compressible flow fields with complex nonlinear…
cs.LG2024★ 1 cited
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs
Bilal Mufti, Christian Perron, Dimitri N. Mavris
In the early stages of aerospace design, reduced order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-que…