research
∙
09/18/2023
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
The modeling of multistage manufacturing systems (MMSs) has attracted in...
share
research
∙
06/17/2023
Variational Sequential Optimal Experimental Design using Reinforcement Learning
We introduce variational sequential Optimal Experimental Design (vsOED),...
research
∙
06/17/2023
FP-IRL: Fokker-Planck-based Inverse Reinforcement Learning – A Physics-Constrained Approach to Markov Decision Processes
Inverse Reinforcement Learning (IRL) is a compelling technique for revea...
research
∙
05/07/2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
On top of machine learning models, uncertainty quantification (UQ) funct...
research
∙
03/25/2023
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis
Data-driven artificial intelligence models require explainability in int...
research
∙
03/23/2023
Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation
In the era of industrial big data, prognostics and health management is ...
research
∙
07/13/2022
Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference
Bayesian inference allows the transparent communication of uncertainty i...
research
∙
10/28/2021
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
We present a mathematical framework and computational methods to optimal...
research
∙
07/19/2021
Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning
We describe a novel end-to-end approach using Machine Learning to recons...
research
∙
01/21/2018
Embedded Model Error Representation for Bayesian Model Calibration
Model error estimation remains one of the key challenges in uncertainty ...
research
∙
01/06/2018
Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation...
research
∙
07/28/2017
Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions
Compressive sensing is a powerful technique for recovering sparse soluti...
research
∙
04/28/2016
Sequential Bayesian optimal experimental design via approximate dynamic programming
The design of multiple experiments is commonly undertaken via suboptimal...
research
∙
08/20/2011
