2 citations · 3 across the 3 of their papers we have counts for
3 papers
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…
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…
Manifold Alignment-Based Multi-Fidelity Reduced-Order Modeling Applied to Structural Analysis
Christian Perron, Darshan Sarojini, Dushhyanth Rajaram +2
This work presents the application of a recently developed parametric, non-intrusive, and multi-fidelity reduced-order modeling method on high-dimensional displacement and stress f…