458 citations · 1.4k across the 32 of their papers we have counts for
6 papers · 1 filter
Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
Zhiyi Chen, Harshal Maske, Devesh Upadhyay +3
This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed…
Optimal experimental design: Formulations and computations
Xun Huan, Jayanth Jagalur, Youssef Marzouk
Questions of `how best to acquire data' are essential to modeling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental desi…
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…
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
Shijie Zhong, Wanggang Shen, Tommie Catanach +1
Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data. Under a Bayesian approach, conventional OED maximizes the…
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
Jeremiah Hauth, Cosmin Safta, Xun Huan +2
The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at mode…
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…