Changhong Mou

According to our database, Changhong Mou authored at least 23 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts.

Jiaxiao Xu, Changhong Mou, Yeyu Zhang, Fengxiang He

CoRR, May, 2026

Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning.

Binghang Lu, Zheyuan Deng, Runyu Zhang, Bing Hu, Yunhan Zhao, Yuan Tian, Changhong Mou, Guang Lin, Xiaomin Li

CoRR, May, 2026

AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training.

Binghang Lu, Runyu Zhang, Changhong Mou, Na Li, Guang Lin

CoRR, May, 2026

Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition.

Changhong Mou, Binghang Lu, Guang Lin

CoRR, February, 2026

iPINNER: An iterative physics-informed neural network with ensemble Kalman filter.

Binghang Lu, Changhong Mou, Guang Lin

J. Comput. Phys., 2026

2025

PIP² Net: Physics-informed Partition Penalty Deep Operator Network.

Hongjin Mi, Huiqiang Lun, Changhong Mou, Yeyu Zhang

CoRR, December, 2025

PAS-Net: Physics-informed Adaptive Scale Deep Operator Network.

Changhong Mou, Yeyu Zhang, Xuewen Zhu, Qiao Zhuang

CoRR, November, 2025

An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network.

Binghang Lu, Changhong Mou, Guang Lin

CoRR, September, 2025

MoPINNEnKF: Iterative Model Inference using generic-PINN-based ensemble Kalman filter.

Binghang Lu, Changhong Mou, Guang Lin

CoRR, June, 2025

Energy-dissipative evolutionary Kolmogorov-Arnold networks for complex PDE systems.

Guang Lin, Changhong Mou, Jiahao Zhang

J. Comput. Phys., 2025

2024

A Stochastic Precipitating Quasi-Geostrophic Model.

Nan Chen, Changhong Mou, Leslie M. Smith, Yeyu Zhang

CoRR, 2024

2023

An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems.

Changhong Mou, Nan Chen, Traian Iliescu

J. Comput. Phys., November, 2023

2022

Verifiability of the Data-Driven Variational Multiscale Reduced Order Model.

Birgul Koc, Changhong Mou, Honghu Liu, Zhu Wang, Gianluigi Rozza, Traian Iliescu

J. Sci. Comput., 2022

A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations.

Dylan Park, Changhong Mou, Honghu Liu, Adrian Sandu, Traian Iliescu

CoRR, 2022

An Energy-Based Lengthscale for Reduced Order Models of Turbulent Flows.

Changhong Mou, Elia Merzari, Omer San, Traian Iliescu

CoRR, 2022

Stochastic Data-Driven Variational Multiscale Reduced Order Models.

Fei Lu, Changhong Mou, Honghu Liu, Traian Iliescu

CoRR, 2022

Reduced Order Model Closures: A Brief Tutorial.

William Snyder, Changhong Mou, Honghu Liu, Omer San, Raffaella De Vita, Traian Iliescu

CoRR, 2022

2021

A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates.

Andrey A. Popov, Changhong Mou, Adrian Sandu, Traian Iliescu

SIAM J. Sci. Comput., 2021

A Numerical Investigation of the Lengthscale in the Mixing-Length Reduced Order Model of the Turbulent Channel Flow.

Changhong Mou, Elia Merzari, Omer San, Traian Iliescu

CoRR, 2021

2020

Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey.

Changhong Mou, Zhu Wang, David R. Wells, Xuping Xie, Traian Iliescu

CoRR, 2020

Data-Driven Variational Multiscale Reduced Order Models.

Changhong Mou, Birgul Koc, Omer San, Traian Iliescu

CoRR, 2020

2019

Data-Driven Correction Reduced Order Models for the Quasi-Geostrophic Equations: A Numerical Investigation.

Changhong Mou, Honghu Liu, David R. Wells, Traian Iliescu

CoRR, 2019

Commutation error in reduced order modeling of fluid flows.

Birgul Koc, Muhammad Mohebujjaman, Changhong Mou, Traian Iliescu

Adv. Comput. Math., 2019