16 citations · 37 across the 6 of their papers we have counts for
6 papers
Matching Urban Flood Sensor Placement to Monitoring Objectives Using Bayesian Optimal Experimental Design
Chen Cheng, Vinh Ngoc Tran, Jiayuan Dong +5
Flood-monitoring sensors are often placed according to coverage, access, or expected inundation. However, the value of a measurement depends on the prediction or decision it is int…
Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning
Zheyu Zhang, Jiayuan Dong, Jie Liu +1
We present GO-CBED, a goal-oriented Bayesian framework for sequential causal experimental design. Unlike conventional approaches that select interventions aimed at inferring the fu…
Variational Bayesian Optimal Experimental Design with Normalizing Flows
Jiayuan Dong, Christian Jacobsen, Mehdi Khalloufi +4
Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte C…
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition
Christian Jacobsen, Jiayuan Dong, Mehdi Khalloufi +4
We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine learning enhancements. As a demon…
Variational Sequential Optimal Experimental Design using Reinforcement Learning
Wanggang Shen, Jiayuan Dong, Xun Huan
We present variational sequential optimal experimental design (vsOED), a novel method for optimally designing a finite sequence of experiments within a Bayesian framework with info…
Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference
Jiayuan Dong, Jiankan Liao, Xun Huan +1
Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available. Howeve…