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Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network
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Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network (2019)Bayesian inference of non-linear multisca…A variable-fidelity hybrid surrogate approach for quantifying uncertainties in the nonlinear response of braided composites (2021)A variable-fidelity hybrid su…A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites (2021)A neural network-aided Bayesi…Design and Analysis of Computer Experiments (1989)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Estimation of spatial uncertainty in material property distributions within heterogeneous structures using optimized convolutional neural networks (2022)Navigating the unknown: Tackling high-dimensional challenges in composite damage modeling with bootstrapping and Bayesian uncertainty quantification (2024)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code (2000)Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters (2014)
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  • 2021A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites27 cited
  • 2021A variable-fidelity hybrid surrogate approach for quantifying uncertainties in the nonlinear response of braided composites24 cited

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