4 papers
Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods
Huchen Yang, Xinghao Dong, Jinlong Wu
Bayesian experimental design (BED) for complex physical systems is often limited by the nested inference required to estimate the expected information gain (EIG) or its gradients.…
Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling
Xinghao Dong, Huchen Yang, Jin-long Wu
Diffusion models recently developed for generative AI tasks can produce high-quality samples while still maintaining diversity among samples to promote mode coverage, providing a p…
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…