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Cited by

• 2018A unified framework for stochastic optimization319 cited
• 2020Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey230 cited
• 2022A state-of-the-art review on uncertainty analysis of rotor systems166 cited
• 2015Exploiting active subspaces to quantify uncertainty in the numerical simulation of the HyShot II scramjet165 cited
• 2015Coherence motivated sampling and convergence analysis of least squares polynomial Chaos regression134 cited
• 2018Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory131 cited
• 2020UQpy: A general purpose Python package and development environment for uncertainty quantification94 cited
• 2019Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation91 cited
• 2016Review of improved Monte Carlo methods in uncertainty-based design optimization for aerospace vehicles88 cited
• 2025A survey on machine learning approaches for uncertainty quantification of engineering systems84 cited
• 2017Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data75 cited
• 2020Embedded Model Error Representation for Bayesian Model Calibration59 cited