Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Huan, Xun"'
Conventional Bayesian optimal experimental design seeks to maximize the expected information gain (EIG) on model parameters. However, the end goal of the experiment often is not to learn the model parameters, but to predict downstream quantities of i
Externí odkaz:
http://arxiv.org/abs/2408.09582
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 design (OED) formalizes these questions and creates computational methods
Externí odkaz:
http://arxiv.org/abs/2407.16212
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 to minimize the quality variations caused by process disturbances in
Externí odkaz:
http://arxiv.org/abs/2407.16933
Autor:
Dong, Jiayuan, Jacobsen, Christian, Khalloufi, Mehdi, Akram, Maryam, Liu, Wanjiao, Duraisamy, Karthik, Huan, Xun
Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood.
Externí odkaz:
http://arxiv.org/abs/2404.13056
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 expected information gain (EIG) on model parameters. However, we are o
Externí odkaz:
http://arxiv.org/abs/2403.18072
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 modeling processes with spatial-temporal complexity. Nevertheless, these h
Externí odkaz:
http://arxiv.org/abs/2402.11179
Autor:
Chen, Zhiyi, Maske, Harshal, Shui, Huanyi, Upadhyay, Devesh, Hopka, Michael, Cohen, Joseph, Lai, Xingjian, Huan, Xun, Ni, Jun
Publikováno v:
Journal of Manufacturing Systems 71 (2023) 609-619
The modeling of multistage manufacturing systems (MMSs) has attracted increased attention from both academia and industry. Recent advancements in deep learning methods provide an opportunity to accomplish this task with reduced cost and expertise. Th
Externí odkaz:
http://arxiv.org/abs/2309.10193
We introduce variational sequential Optimal Experimental Design (vsOED), a new method for optimally designing a finite sequence of experiments under a Bayesian framework and with information-gain utilities. Specifically, we adopt a lower bound estima
Externí odkaz:
http://arxiv.org/abs/2306.10430
Inverse Reinforcement Learning (IRL) is a compelling technique for revealing the rationale underlying the behavior of autonomous agents. IRL seeks to estimate the unknown reward function of a Markov decision process (MDP) from observed agent trajecto
Externí odkaz:
http://arxiv.org/abs/2306.10407
Autor:
Nemani, Venkat, Biggio, Luca, Huan, Xun, Hu, Zhen, Fink, Olga, Tran, Anh, Wang, Yan, Zhang, Xiaoge, Hu, Chao
Publikováno v:
Mechanical Systems and Signal Processing 205 (2023) 110796
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 enabling sound risk assessment and management. The safety and reliability imp
Externí odkaz:
http://arxiv.org/abs/2305.04933