research
∙
06/03/2023
An information field theory approach to Bayesian state and parameter estimation in dynamical systems
Dynamical system state estimation and parameter calibration problems are...
share
research
∙
05/31/2023
Learning to solve Bayesian inverse problems: An amortized variational inference approach
Inverse problems, i.e., estimating parameters of physical models from ex...
research
∙
01/18/2023
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification
Data-driven approaches coupled with physical knowledge are powerful tech...
research
∙
02/25/2022
Bayesian Inference of Fiber Orientation and Polymer Properties in Short Fiber-Reinforced Polymer Composites
We present a Bayesian methodology to infer the elastic modulus of the co...
research
∙
02/08/2022
Physics-informed neural networks for solving parametric magnetostatic problems
The optimal design of magnetic devices becomes intractable using current...
research
∙
05/12/2021
Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known
Probabilistic machine learning models are often insufficient to help wit...
research
∙
02/07/2021
Exploratory Data Analysis for Airline Disruption Management
Reliable platforms for data collation during airline schedule operations...
research
∙
10/05/2020
Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty
Excessive loads near wounds produce pathological scarring and other comp...
research
∙
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...
research
∙
05/04/2019
Automated Building Image Extraction from 360-degree Panoramas for Post-Disaster Evaluation
After a disaster, teams of structural engineers collect vast amounts of ...
research
∙
03/28/2019
Towards a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process
Systems engineering processes coordinate the effort of different individ...
research
∙
03/21/2019
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints
Thermal preferences vary from person to person and may change over time....
research
∙
03/16/2019
A Principal-Agent Model of Systems Engineering Processes with Application to Satellite Design
We present a principal-agent model of a one-shot, shallow, systems engin...
research
∙
02/27/2019
Deep active subspaces -- a scalable method for high-dimensional uncertainty propagation
A problem of considerable importance within the field of uncertainty qua...
research
∙
07/26/2018
Deriving Information Acquisition Criteria For Sequentially Inferring The Expected Value Of A Black-Box Function
Acquiring information about noisy expensive black-box functions (compute...
research
∙
02/02/2018
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
State-of-the-art computer codes for simulating real physical systems are...
research
∙
10/21/2014
