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Efficient forward and inverse uncertainty quantification for dynamical systems based on dimension reduction and Kriging surrogate modeling in functional space
14 references·0 citing·36 second hop
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References

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  • 2009Time series analysis forecasting and control2,585 cited
  • 2014UQLab: A Framework for Uncertainty Quantification in Matlab1,018 cited
  • 2021Engineering analysis with probability boxes: A review on computational methods142 cited
  • 2018Uncertainty quantification in multiscale simulation of woven fiber composites131 cited
  • 2021A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis113 cited
  • 2016SURROGATE MODELING FOR STOCHASTIC DYNAMICAL SYSTEMS BY COMBINING NONLINEAR AUTOREGRESSIVE WITH EXOGENOUS INPUT MODELS AND POLYNOMIAL CHAOS EXPANSIONS76 cited
  • 2020Fast surrogate modeling using dimensionality reduction in model inputs and field output: Application to additive manufacturing64 cited
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  • 2021A global surrogate model for high-dimensional structural systems based on partial least squares and Kriging32 cited
  • 2014Sparse polynomial chaos expansions of vector-valued response quantities31 cited
  • 2022An efficient method for time-dependent reliability problems with high-dimensional outputs based on adaptive dimension reduction strategy and surrogate model28 cited
  • 2024A dimension reduction-based Kriging modeling method for high-dimensional time-variant uncertainty propagation and global sensitivity analysis

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  • 2022A feature mapping strategy of metamodelling for nonlinear stochastic dynamical systems with low to high-dimensional input uncertainties20 cited
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