83 citations · 117 across the 14 of their papers we have counts for
15 papers
Simulation and Data Assimilation in an Idealized Coupled Atmosphere-Ocean-Sea Ice Floe Model with Cloud Effects
Changhong Mou, Samuel N. Stechmann, Nan Chen
Sea ice plays a crucial role in the climate system, particularly in the Marginal Ice Zone (MIZ), a transitional area consisting of fragmented ice between the open ocean and consoli…
A Stochastic Precipitating Quasi-Geostrophic Model
Nan Chen, Changhong Mou, Leslie M. Smith +1
Efficient and effective modeling of complex systems, incorporating cloud physics and precipitation, is essential for accurate climate modeling and forecasting. However, simulating…
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…
A Numerical Investigation of the Lengthscale in the Mixing-Length Reduced Order Model of the Turbulent Channel Flow
Changhong Mou, Elia Merzari, Omer San +1
In this paper, we propose a novel reduced order model (ROM) lengthscale definition that is based on energy distribution arguments. This novel ROM lengthscale is fundamentally diffe…
Verifiability of the Data-Driven Variational Multiscale Reduced Order Model
Birgul Koc, Changhong Mou, Honghu Liu +3
In this paper, we focus on the mathematical foundations of reduced order model (ROM) closures. First, we extend the verifiability concept from large eddy simulation to the ROM sett…
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…
A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates
Andrey A Popov, Changhong Mou, Traian Iliescu +1
This work develops a new multifidelity ensemble Kalman filter (MFEnKF) algorithm based on linear control variate framework. The approach allows for rigorous multifidelity extension…
Data-Driven Variational Multiscale Reduced Order Models
Changhong Mou, Birgul Koc, Omer San +2
We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to…
Data-Driven Correction Reduced Order Models for the Quasi-Geostrophic Equations: A Numerical Investigation
Changhong Mou, Honghu Liu, David R. Wells +1
This paper investigates the recently introduced data-driven correction reduced order model (DDC-ROM) in the numerical simulation of the quasi-geostrophic equations. The DDC-ROM use…
Commutation Error in Reduced Order Modeling of Fluid Flows
Birgul Koc, Muhammad Mohebujjaman, Changhong Mou +1
For reduced order models (ROMs) of fluid flows, we investigate theoretically and computationally whether differentiation and ROM spatial filtering commute, i.e., whether the commut…