14 citations · 55 across the 10 of their papers we have counts for
8 papers · 1 filter
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…