2 citations · 4 across the 3 of their papers we have counts for
5 papers
Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey
Changhong Mou, Zhu Wang, David R. Wells +2
Reduced order models (ROMs) are computational models whose dimension is significantly lower than those obtained through classical numerical discretizations (e.g., finite element, f…
Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network
Xuping Xie, Clayton G. Webster, Traian Iliescu
Developing accurate, efficient, and robust closure models is essential in the construction of reduced order models (ROMs) for realistic nonlinear systems, which generally require d…
Analytic Continuation of Noisy Data Using Adams Bashforth ResNet
Xuping Xie, Feng Bao, Thomas Maier +1
We propose a data-driven learning framework for the analytic continuation problem in numerical quantum many-body physics. Designing an accurate and efficient framework for the anal…
Non-intrusive inference reduced order model for fluids using linear multistep neural network
Xuping Xie, Guannan Zhang, Clayton G. Webster
In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid d…
Lagrangian Data-Driven Reduced Order Modeling of Finite Time Lyapunov Exponents
Xuping Xie, Peter J. Nolan, Shane D. Ross +2
There are two main strategies for improving the projection-based reduced order model (ROM) accuracy: (i) improving the ROM, i.e., adding new terms to the standard ROM; and (ii) imp…