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Recent progress of uncertainty quantification in small-scale materials science
16 references·3 citing·28 second hop
Recent progress of uncertainty quantification in small-scale materials science (2020)Recent progress of uncertainty quantifica…Structural Reliability Analysis and Prediction (2017)Structural Reliability Analys…Uncertainty Quantification: Theory, Implementation, and Applications (2013)Uncertainty Quantification: T…An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations (2009)An adaptive hierarchical spar…Reliability-based Structural Design (2006)Reliability-based Structural …Uncertainty propagation using Wiener–Haar expansions (2004)Uncertainty propagation using…An exploration of alternative approaches to the representation of uncertainty in model predictions (2004)An exploration of alternative…Modeling uncertainty in flow simulations via generalized polynomial chaos (2003)Modeling uncertainty in flow …The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Structural Reliability — Analysis and Prediction (2001)Structural Reliability — Anal…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…Ingredients for a general purpose stochastic finite elements implementation (1999)Ingredients for a general pur…Structural Reliability Methods (1996)Structural Reliability MethodsStochastic Finite Elements: A Spectral Approach (1991)Stochastic Finite Elements: A…Importance Sampling for Stochastic Simulations (1989)Importance Sampling for Stoch…Factor Analysis: An Applied Approach (1985)Factor Analysis: An Applied A…A mathematical theory of evidence (1977)A mathematical theory of evid…Uncertainty Quantification of Microstructures: A Perspective on Forward and Inverse Problems for Mechanical Properties of Aerospace Materials (2024)Uncertainty Quantification of…Performance prediction of gas turbine blade with multi-source random factors using active learning-based neural network (2024)Performance prediction of gas…Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods (2023)Multi-fidelity microstructure…Probability Concepts in Engineering Planning and Design (1975)Structural reliability under combined random load sequences (1978)Aleatory or epistemic? Does it matter? (2008)Uncertainties in risk analysis: Six levels of treatment (1996)Reliability analysis—a review and some perspectives (2001)Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design (2004)Global sensitivity analysis using polynomial chaos expansions (2007)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Relative Entropy Based Method for Probabilistic Sensitivity Analysis in Engineering Design (2005)Adaptive Designs of Experiments for Accurate Approximation of a Target Region (2010)Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles (2011)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)A review of techniques for parameter sensitivity analysis of environmental models (1994)Importance measures in global sensitivity analysis of nonlinear models (1996)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Likelihood ratio gradient estimation for stochastic systems (1990)Fast simulation of rare events in queueing and reliability models (1995)Simulation-based optimization of Markov reward processes (2001)An adaptive multi-element generalized polynomial chaos method for stochastic differential equations (2005)Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics (2008)Fast Numerical Methods for Stochastic Computations: A Review (2009)k-order additive discrete fuzzy measures and their representation (1997)A new distance between two bodies of evidence (2001)A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011)A unified framework for stochastic optimization (2018)Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey (2020)A state-of-the-art review on uncertainty analysis of rotor systems (2022)
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References

  • 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code8,233 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2017Structural Reliability Analysis and Prediction3,780 cited
  • 1996Structural Reliability Methods2,192 cited
  • 1977A mathematical theory of evidence1,783 cited
  • 2003Modeling uncertainty in flow simulations via generalized polynomial chaos1,428 cited
  • 2013Uncertainty Quantification: Theory, Implementation, and Applications1,147 cited
  • 1989Importance Sampling for Stochastic Simulations635 cited
  • 2001Structural Reliability — Analysis and Prediction623 cited
  • 2006Reliability-based Structural Design588 cited
  • 2009An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations447 cited
  • 2004Uncertainty propagation using Wiener–Haar expansions438 cited
  • 1999Ingredients for a general purpose stochastic finite elements implementation401 cited
  • 1985Factor Analysis: An Applied Approach401 cited

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  • 2004An exploration of alternative approaches to the representation of uncertainty in model predictions362 cited
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    • 2024Uncertainty Quantification of Microstructures: A Perspective on Forward and Inverse Problems for Mechanical Properties of Aerospace Materials20 cited
    • 2023Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods15 cited
    • 2024Performance prediction of gas turbine blade with multi-source random factors using active learning-based neural network15 cited