More Like this

1. Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach

   https://doi.org/10.1115/1.4052270  

   Ye, Jiahui ; Mahmoudi, Mohamad ; Karayagiz, Kubra ; Johnson, Luke ; Seede, Raiyan ; Karaman, Ibrahim ; Arroyave, Raymundo ; Elwany, Alaa ( March 2022 , ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg)

   Abstract Modeling and simulation for additive manufacturing (AM) are critical enablers for understanding process physics, conducting process planning and optimization, and streamlining qualification and certification. It is often the case that a suite of hierarchically linked (or coupled) simulation models is needed to achieve the above tasks, as the entirety of the complex physical phenomena relevant to the understanding of process-structure-property-performance relationships in the context of AM precludes the use of a single simulation framework. In this study using a Bayesian network approach, we address the important problem of conducting uncertainty quantification (UQ) analysis for multiple hierarchical models to establish process-microstructure relationships in laser powder bed fusion (LPBF) AM. More significantly, we present the framework to calibrate and analyze simulation models that have experimentally unmeasurable variables, which are quantities of interest predicted by an upstream model and deemed necessary for the downstream model in the chain. We validate the framework using a case study on predicting the microstructure of a binary nickel-niobium alloy processed using LPBF as a function of processing parameters. Our framework is shown to be able to predict segregation of niobium with up to 94.3% prediction accuracy on test data. 

   more » « less
2. A Bayesian approach to sequential monitoring of nonlinear profiles using wavelets: Wavelet-Based Bayesian Profile Monitoring

   https://doi.org/10.1002/qre.2409  

   Varbanov, Roumen ; Chicken, Eric ; Linero, Antonio ; Yang, Yun ( April 2019 , Quality and Reliability Engineering International)
3. Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data: Bayesian Multi-Modal VAR Model

   https://doi.org/10.1002/hbm.23456  

   Chiang, Sharon ; Guindani, Michele ; Yeh, Hsiang J. ; Haneef, Zulfi ; Stern, John M. ; Vannucci, Marina ( March 2017 , Human Brain Mapping)
4. Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression

   https://doi.org/10.1214/21-BA1257  

   Lewis, John R. ; MacEachern, Steven N. ; Lee, Yoonkyung ( January 2021 , Bayesian Analysis)

   null (Ed.)
5. Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation

   https://doi.org/10.1007/s11517-022-02685-y  

   Nieto Ramos, Alejandro ; Fenton, Flavio H. ; Cherry, Elizabeth M. ( January 2023 , Medical & Biological Engineering & Computing)