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Keywords: Gaussian process
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Journal Articles
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
Article Type: Research Papers
J. Mech. Des. February 2022, 144(2): 021706.
Paper No: MD-21-1275
Published Online: September 21, 2021
... 2021 inverse design invertible neural network machine learning probabilistic modeling Gaussian process gas turbine aerodynamic design data-driven design design automation design methodology generative design Advanced Research Projects Agency 10.13039/100009224 DE-AR0001204...
Journal Articles
Journal Articles
Article Type: Research Papers
J. Mech. Des. March 2021, 143(3): 031713.
Paper No: MD-20-1545
Published Online: January 29, 2021
... representation Gaussian process topological domain conditional simulation uncertainty analysis To enable stochastic analysis (e.g., Monte Carlo simulation) of the network system, an effective way of generating random realization from the uncertainty representation model is needed. As discussed...
Journal Articles
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
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
Article Type: Research-Article
J. Mech. Des. March 2014, 136(3): 031005.
Paper No: MD-13-1043
Published Online: January 10, 2014
... surface, y m ( x ) , and to determine the parameters of the bias correction function. The hyperparameters of the Gaussian process are briefly listed in Appendix A. The results are shown in case 1 through case 6 in Table 1 . Column 4 shows the standard deviations obtained from the bias...
Journal Articles
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
... by posterior standard deviations) by an amount that ranges from minimal to substantial, depending on the characteristics of the specific responses that are combined. 27 08 2011 03 07 2012 21 09 2012 28 09 2012 multiple responses Gaussian process model updating calibration...