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Keywords: uncertainty quantification
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Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. January 2023, 145(1): 012001.
Paper No: MD-22-1210
Published Online: October 7, 2022
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. September 2022, 144(9): 091704.
Paper No: MD-21-1740
Published Online: June 13, 2022
... , “ 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. September 2022, 144(9): 091705.
Paper No: MD-21-1780
Published Online: June 13, 2022
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. September 2021, 143(9): 091701.
Paper No: MD-20-1678
Published Online: February 11, 2021
... 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...
Journal Articles
Anton van Beek, Umar Farooq Ghumman, Joydeep Munshi, Siyu Tao, TeYu Chien, Ganesh Balasubramanian, Matthew Plumlee, Daniel Apley, Wei Chen
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. March 2021, 143(3): 031709.
Paper No: MD-20-1421
Published Online: December 15, 2020
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. May 2021, 143(5): 051702.
Paper No: MD-19-1898
Published Online: October 28, 2020
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Technical Briefs
J. Mech. Des. July 2020, 142(7): 074501.
Paper No: MD-19-1674
Published Online: March 3, 2020
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. May 2020, 142(5): 051701.
Paper No: MD-18-1475
Published Online: November 11, 2019
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research-Article
J. Mech. Des. October 2019, 141(10): 101404.
Paper No: MD-18-1592
Published Online: July 10, 2019
...) 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Special Section: Methods For Uncertainty Characterizations In Existing Models Through Uncertainly Quantification Or Calibration
J. Mech. Des. October 2012, 134(10): 100909.
Published Online: September 28, 2012
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Special Section: Methods For Uncertainty Characterizations In Existing Models Through Uncertainly Quantification Or Calibration
J. Mech. Des. October 2012, 134(10): 100908.
Published Online: September 28, 2012
... 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. August 2012, 134(8): 081003.
Published Online: July 23, 2012
... ( 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...
Journal Articles
Journal:
Journal of Mechanical Design
Article Type: Research Papers
J. Mech. Des. February 2008, 130(2): 021101.
Published Online: December 27, 2007
... 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...