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Keywords: Gaussian process
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Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. May 2024, 146(5): 051709.
Paper No: MD-23-1484
Published Online: January 29, 2024
... Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 15 07 2023 08 12 2023 11 12 2023 29 01 2024 machine learning design regression Gaussian process data-driven design design automation design optimization...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. June 2024, 146(6): 061703.
Paper No: MD-23-1380
Published Online: December 18, 2023
... ,” 2020 Conference on Neural Information Processing Systems , Chicago, IL , Dec. 6–12 , PMLR, pp. 3 – 26 . [10] Song , J. , Chen , Y. , and Yue , Y. , 2019 , “ A General Framework for Multi-fidelity Bayesian Optimization With Gaussian Processes ,” Proceedings of the 22nd...
Includes: Supplementary data
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. October 2023, 145(10): 101705.
Paper No: MD-22-1758
Published Online: July 19, 2023
... 2023 Bayesian optimization multi-objective optimization Gaussian process acquisition function data-driven design design optimization machine learning metamodeling simulation-based design The solution of design optimization problems in engineering and science often requires...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. April 2022, 144(4): 041705.
Paper No: MD-21-1373
Published Online: February 22, 2022
... as design variables. Gaussian process (GP) regression models are trained to predict the relationship between latent features and properties for property-driven optimization. The optimal structural designs are reconstructed by mapping the optimized latent feature values to the original image space. Compared...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. February 2022, 144(2): 021706.
Paper No: MD-21-1275
Published Online: September 21, 2021
.... Risk and Uncert in Eng. Syst. Part B Mech. Eng. , 6 ( 3 ), p. 030904 . 10.1115/1.4046747 [25] Pandita , P. , Tsilifis , P. , Ghosh , S. , and Wang , L. , 2021 , “ Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. February 2022, 144(2): 021703.
Paper No: MD-21-1235
Published Online: September 15, 2021
...Liwei Wang; Suraj Yerramilli; Akshay Iyer; Daniel Apley; Ping Zhu; Wei Chen Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. March 2021, 143(3): 031713.
Paper No: MD-20-1545
Published Online: January 29, 2021
...@uconn.edu Email: hongyi.3.xu@uconn.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . network uncertainty representation Gaussian process topological domain conditional simulation uncertainty analysis A network...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. March 2021, 143(3): 031708.
Paper No: MD-20-1410
Published Online: November 13, 2020
... classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or “distance” measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. March 2021, 143(3): 031702.
Paper No: MD-20-1043
Published Online: November 10, 2020
... and stochastic processes. By randomly sampling the time-independent random variables, multiple LSTM networks can be trained and leveraged with the Gaussian process (GP) regression to construct a global surrogate model for the time-dependent limit state function. In detail, a set of augmented data is first...
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Mech. Des. March 2014, 136(3): 031005.
Paper No: MD-13-1043
Published Online: January 10, 2014
.... In addition, the accuracy of any response surface is inherently unpredictable. This paper employs the Gaussian process based model bias correction method to quantify the data uncertainty and subsequently improve the accuracy of a response surface model. An adaptive response surface updating algorithm...
Journal Articles
Publisher: ASME
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
... , “ Gaussian Process Models for Computer Experiments With Qualitative and Quantitative Factors ,” Technometrics , 50 ( 3 ), pp. 383 – 396 . 10.1198/004017008000000262 4 McMillan , N. , Sacks , J. , Welch , W. , and Gao , F. , 1999 , “ Analysis of Protein Activity Data by Gaussian...