83 citations · 117 across the 14 of their papers we have counts for
6 papers · 1 filter
Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations
Changhong Mou, Leslie M. Smith, Nan Chen
A hybrid data assimilation algorithm is developed for complex dynamical systems with partial observations. The method starts with applying a spectral decomposition to the entire sp…
A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations
Dylan Park, Changhong Mou, Honghu Liu +2
We propose, analyze, and investigate numerically a novel two-level Galerkin reduced order model (2L-ROM) for the efficient and accurate numerical simulation of the steady Navier-St…
An Energy-Based Lengthscale for Reduced Order Models of Turbulent Flows
Changhong Mou, Elia Merzari, Omer San +1
In this paper, we propose a novel reduced order model (ROM) lengthscale that is constructed by using energy distribution arguments. The new energy-based ROM lengthscale is fundamen…
Stochastic Data-Driven Variational Multiscale Reduced Order Models
Fei Lu, Changhong Mou, Honghu Liu +1
Trajectory-wise data-driven reduced order models (ROMs) tend to be sensitive to training data, and thus lack robustness. We propose to construct a robust stochastic ROM closure (S-…
An Efficient Data-Driven Multiscale Stochastic Reduced Order Modeling Framework for Complex Systems
Changhong Mou, Nan Chen, Traian Iliescu
Suitable reduced order models (ROMs) are computationally efficient tools in characterizing key dynamical and statistical features of nature. In this paper, a systematic multiscale…
Reduced Order Model Closures: A Brief Tutorial
William Snyder, Changhong Mou, Honghu Liu +3
In this paper, we present a brief tutorial on reduced order model (ROM) closures. First, we carefully motivate the need for ROM closure modeling in under-resolved simulations. Then…