458 citations · 1.4k across the 32 of their papers we have counts for
6 papers · 1 filter
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
Zhiyi Chen, Harshal Maske, Huanyi Shui +6
The modeling of multistage manufacturing systems (MMSs) has attracted increased attention from both academia and industry. Recent advancements in deep learning methods provide an o…
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…
FP-IRL: Fokker--Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
Chengyang Huang, Siddhartha Srivastava, Kenneth K. Y. Ho +4
Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an unknown reward function from observed…
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
Venkat Nemani, Luca Biggio, Xun Huan +6
On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enablin…
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis
Joseph Cohen, Xun Huan, Jun Ni
Data-driven artificial intelligence models require explainability in intelligent manufacturing to streamline adoption and trust in modern industry. However, recently developed expl…
Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation
Joseph Cohen, Xun Huan, Jun Ni
In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs.…