458 citations · 1.4k across the 32 of their papers we have counts for
33 papers
Active Pilot Sampling for Multi-fidelity Estimation in Stochastic Optimization
Thomas E. Coons, Aniket Jivani, Xun Huan
Stochastic optimization with high-fidelity nonlinear models is often computationally prohibitive because each objective evaluation requires sampling-based uncertainty quantificatio…
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…
Variational Goal-Oriented Optimal Experimental Design for Mixed-Distribution Quantities of Interest: Application to Ship Roll Safety
Chen Cheng, Xun Huan, Yulin Pan
Goal-oriented optimal experimental design (GO-OED) selects experiments according to the expected information gain (EIG) about a quantity of interest (QoI) rather than the full para…
Bayesian Variational System Identification with Weak-Form Residual Likelihoods
Chengyang Huang, Siddhartha Srivastava, Krishna Garikipati +1
We consider system identification for discovering parameterized operators in governing partial differential equations (PDEs) from noisy spatiotemporal data. Building on variational…
Optimal Stopping for Sequential Bayesian Experimental Design
Chen Cheng, Xun Huan
Sequential Bayesian experimental design is often formulated as a fixed-horizon policy optimization problem, in which the number of experiments is specified before data collection b…
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…
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.…
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…
Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow
Yuan Yao, Xun Huan, Jesse Capecelatro
Particle deposition in fully-developed turbulent pipe flow is quantified taking into account uncertainty in electric charge, van der Waals strength, and temperature effects. A fram…
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Wanggang Shen, Xun Huan
We present a mathematical framework and computational methods to optimally design a finite number of sequential experiments. We formulate this sequential optimal experimental desig…
Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning
Maria Han Veiga, Xi Meng, Oleg Y. Gnedin +2
We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra…
A Perspective on Regression and Bayesian Approaches for System Identification of Pattern Formation Dynamics
Zhenlin Wang, Bowei Wu, Krishna Garikipati +1
We present two approaches to system identification, i.e. the identification of partial differential equations (PDEs) from measurement data. The first is a regression-based Variatio…
Variational system identification of the partial differential equations governing microstructure evolution in materials: Inference over sparse and spatially unrelated data
Z. Wang, X. Huan, K. Garikipati
Pattern formation is a widely observed phenomenon in diverse fields including materials physics, developmental biology and ecology, among many others. The physics underlying the pa…
Variational system identification of the partial differential equations governing pattern-forming physics: Inference under varying fidelity and noise
Zhenlin Wang, Xun Huan, Krishna Garikipati
We present a contribution to the field of system identification of partial differential equations (PDEs), with emphasis on discerning between competing mathematical models of patte…