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03/15/2023
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Modern computational methods, involving highly sophisticated mathematica...
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08/14/2020
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems
Industrial dynamical systems often exhibit multi-scale response due to m...
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08/08/2020
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes
Modern day engineering problems are ubiquitously characterized by sophis...
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08/05/2020
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
We present a Bayesian approach to identify optimal transformations that ...
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03/26/2020
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Industrial applications frequently pose a notorious challenge for state-...
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12/16/2019
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design
Estimating arbitrary quantities of interest (QoIs) that are non-linear o...
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07/25/2019
Towards Scalable Gaussian Process Modeling
Numerous engineering problems of interest to the industry are often char...
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07/26/2018