... of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly...

... of spatial-varied parameters to predict the structural response of interest. Direct Monte Carlo methods are infeasible for uncertainty quantification and reliability assessment of such systems as they require a large number of forward model evaluations to obtain convergent statistics. To alleviate...

... Solid Freeform Fabrication Symposium—An Additive Manufacturing Conference , Austin, TX , Aug. 10–12 . [11] Hu , Z. , and Mahadevan , S. , 2017 , “ Uncertainty Quantification and Management in Additive Manufacturing: Current Status, Needs, and Opportunities ,” Int. J. Adv. Manuf...

... to be reliable and useful for process parameter optimization and control, these uncertainties need to be quantified first [ 5 ]. Uncertainty quantification (UQ) in AM is a relatively new area of research. Some studies have focused on forward propagation of uncertainty and quantification of the contribution...

...: piyush.pandita@ge.com Email: sayan.ghosh1@ge.com Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . sequential optimal experimental design deep reinforcement learning uncertainty quantification Gaussian processes data-driven design...

... (GP) epistemic uncertainty confidence-based design optimization (CBDO) surrogate modeling reliability analysis uncertainty quantification Over the past few decades, reliability-based design optimization (RBDO) has been extensively developed and successfully applied to engineering...

... ( 1 ), pp. 124 – 127 . 10.1214/aoms/1177729893 [42] Jiang , Z. , Apley , D. W. , and Chen , W. , 2015 , “ Surrogate Preposterior Analyses for Predicting and Enhancing Identifiability in Model Calibration ,” Int. J. Uncertainty Quantification , 5 ( 4 ), pp. 341 – 359...

.../00401706.2013.860918 [17] Picheny , V. , 2015 , “ Multiobjective Optimization Using Gaussian Process Emulators Via Stepwise Uncertainty Reduction ,” Stat. Comput. , 25 ( 6 ), pp. 1265 – 1280 . 10.1007/s11222-014-9477-x [18] Tuo , R. , and Wang , W. , 2020 , Uncertainty quantification...

... oscillating function negative correlation uncertainty quantification A random field (aka. random process, stochastic process) is a collection of random variables indexed in the spatial or temporal domain. Let us define a probability space as { S, F, P }, where S represents the input domain (e.g...

... Representation of Uncertainty ,” 19th AIAA Applied Aerodynamics Conference , Seattle, WA , p. 1645 . [2] Wei , D. L. , Cui , Z. S. , and Chen , J. , 2008 , “ Uncertainty Quantification Using Polynomial Chaos Expansion With Points of Monomial Cubature Rules ,” Comput. Struct. , 86 ( 23...

.... 26 07 2018 28 05 2019 29 05 2019 optimal experimental design Kullback–Leibler divergence uncertainty quantification information gain mutual information Gaussian processes Bayesian inference Engineering problems require either computationally intensive computer codes...

... multiple responses Gaussian process model updating calibration identifiability uncertainty quantification Multiple response emulator Quantification of model uncertainty is important to better understand how well a computer model represents physical reality. Two primary sources of uncertainty...

.... calibration identifiability model updating uncertainty quantification Kriging Gaussian processes Uncertainty is ubiquitous in engineering design. Although recent years have seen a proliferation of research in design under uncertainty [( 1 2 3 4 5 )], the majority of the uncertainty analysis...

... systems. Ultimately, if a robust system design is to be achieved, uncertainties must be accounted for up-front during the design process. design optimization dynamic optimization nonlinear programming multi-objective optimization multibody dynamics uncertainty quantification generalized...

... experiments and the computer model, a Bayesian approach is employed to develop a prediction model as the replacement of the original computer model for the purpose of design. Based on the uncertainty quantification with the Bayesian prediction and, subsequently, that of a design objective, some decision...