14 citations · 55 across the 10 of their papers we have counts for
10 papers
A Lagrangian Conditional Gaussian Koopman Network for Data Assimilation and Prediction
Zhongrui Wang, Chuanqi Chen, Jin-Long Wu +1
Lagrangian data assimilation aims to recover hidden Eulerian flow fields from sparse, indirect observations of moving tracers. This problem is challenging because tracer trajectori…
Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distance
Huchen Yang, Xinghao Dong, Jin-Long Wu
Designing experiments that systematically gather data from complex physical systems is central to accelerating scientific discovery. While Bayesian experimental design (BED) provid…
Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models
Xinghao Dong, Huchen Yang, Jin-Long Wu
We propose a latent score-based generative AI framework for learning stochastic, non-local closure models and constitutive laws in nonlinear dynamical systems of computational mech…
Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approach
Huchen Yang, Xinghao Dong, Jin-Long Wu
Bayesian experimental design (BED) offers a principled framework for optimizing data acquisition by leveraging probabilistic inference. However, practical implementations of BED ar…
Active Learning of Model Discrepancy with Bayesian Experimental Design
Huchen Yang, Chuanqi Chen, Jin-Long Wu
Digital twins have been actively explored in many engineering applications, such as manufacturing and autonomous systems. However, model discrepancy is ubiquitous in most digital t…
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation
Chuanqi Chen, Nan Chen, Yinling Zhang +1
Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents sig…
Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator
Xinghao Dong, Chuanqi Chen, Jin-Long Wu
Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and the earth system, for which direct numerical simulation that resolves all s…
CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
Chuanqi Chen, Nan Chen, Jin-Long Wu
A new knowledge-based and machine learning hybrid modeling approach, called conditional Gaussian neural stochastic differential equation (CGNSDE), is developed to facilitate modeli…
Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods
Chuanqi Chen, Jin-Long Wu
Data-driven modeling techniques have been explored in the spatial-temporal modeling of complex dynamical systems for many engineering applications. However, a systematic approach i…
CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
Chuanqi Chen, Nan Chen, Jin-Long Wu
Regime switching is ubiquitous in many complex dynamical systems with multiscale features, chaotic behavior, and extreme events. In this paper, a causation entropy boosting (CEBoos…