... to Identify Critical Variables for Laser Powder Bed Fusion ,” Annual International 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...

... , “ Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion ,” ASCE–ASME J. Risk Uncert. Eng. Syst. Part B: Mech. Eng. , 8 ( 1 ), p. 011106 . 10.1115/1.4051748 [8] Zhang , Y. , Karnati , S. , Nag , S. , Johnson , N. , Khan...

... the performance of the proposed algorithm on a synthetic problem and a challenging high-dimensional engineering problem. sequential optimal experimental design deep reinforcement learning uncertainty quantification Gaussian processes data-driven design design of experiments design optimization machine...

... for Verification, Validation, and Uncertainty Quantification in Scientific Computing ,” Comput. Methods Appl. Mech. Eng. , 200 ( 25–28 ), pp. 2131 – 2144 . 10.1016/j.cma.2011.03.016 [13] Lee , G. , Kim , W. , Oh , H. , Youn , B. D. , and Kim , N. H. , 2019 , “ Review of Statistical...

... uncertainty quantification surrogate modeling simulation-based design Sampling of computer simulations is a well-studied subject in the field of simulation-based design with applications in engineering [ 1 ], biological [ 2 ], and social sciences [ 3 ]. These research efforts have driven...

... echanical D esign . 17 12 2019 09 06 2020 13 06 2020 28 10 2020 design for manufacturing design of multiscale systems design process multiobjective optimization simulation-based design uncertainty quantification In the design of engineering systems, multiple...

... matrix 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...

... uncertainty quantification dimension reduction decomposition marginal collocation nodes epistemic uncertainty Uncertainties inevitably exist in the entire cycle of structural design, manufacture, and utilization. Compared with single uncertain factor, the coupling effect of multiple uncertain...

...) approach. optimal experimental design Kullback–Leibler divergence uncertainty quantification information gain mutual information Gaussian processes Bayesian inference 26 07 2018 28 05 2019 29 05 2019 Contributed by the Design Automation Committee of ASME...

... 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 that account for differences between a computer model and physical reality...

... and reliable design decision making. The objective of this paper is to closely examine existing mathematical frameworks for model uncertainty quantification and to offer insight into the associated challenges. We argue that model uncertainty quantification as it is typically implemented in a model updating...

... ( 4 ), pp. 369 – 391 . 10.1007/s11044-006-9007-5 24 Cheng , H. , and Sandu , A. , 2009 , “ Efficient Uncertainty Quantification With the Polynomial Chaos Method for Stiff Systems ,” Math. Comput. Simul. , 79 ( 11 ), pp. 3278 – 3295 . 10.1016/j.matcom.2009.05.002 25 Wan...

... 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...