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Building Confidence in Simulation: Applications of EasyVVUQ
7 references·4 citing·38 second hop
Building Confidence in Simulation: Applications of EasyVVUQ (2020)Building Confidence in Simulation: Applic…EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing (2020)EasyVVUQ: A Library for Verif…FabSim: Facilitating computational research through automation on large-scale and distributed e-infrastructures (2016)FabSim: Facilitating computat…Chaospy: An open source tool for designing methods of uncertainty quantification (2015)Chaospy: An open source tool …Verification and Validation in Scientific Computing (2010)Verification and Validation i…Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…Error and uncertainty in modeling and simulation (2002)Error and uncertainty in mode…Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global sensitivity indices fo…Uncertainty Quantification for In Silico Chemistry (2026)Uncertainty Quantification fo…When we can trust computers (and when we can't) (2021)When we can trust computers (…Uncertainty quantification patterns for multiscale models (2021)Uncertainty quantification pa…Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit (2021)Tutorial applications for Ver…Stochastic Finite Elements: A Spectral Approach (1991)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)UQLab: A Framework for Uncertainty Quantification in Matlab (2014)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)Simulation Modeling and Analysis. (1983)Empirical Model Building and Response Surfaces (1988)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Verification, validation, and predictive capability in computational engineering and physics (2004)Calibration, validation, and sensitivity analysis: What's what (2006)Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence (2012)Scientific workflow management and the Kepler system (2005)Snakemake—a scalable bioinformatics workflow engine (2012)The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud (2013)Mastering the scales: a survey on the benefits of multiscale computing software (2019)MDCrow: automating molecular dynamics workflows with large language models (2026)An Introduction to Copulas (1999)Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience (2018)Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques (2019)UQpy: A general purpose Python package and development environment for uncertainty quantification (2020)A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011)1,500 scientists lift the lid on reproducibility (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization (2021)Why Most Published Research Findings Are False (2005)Power failure: why small sample size undermines the reliability of neuroscience (2013)Estimating the reproducibility of psychological science (2015)A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows (2023)A comparative study of uncertainty propagation methods for black-box-type problems (2008)Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification (2009)Uncertainty Quantification: Theory, Implementation, and Applications (2013)Challenges and opportunities in uncertainty quantification for healthcare and biological systems (2025)Global Sensitivity Analysis. The Primer (2007)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Imprecise probabilities in engineering analyses (2013)Combustion kinetic model uncertainty quantification, propagation and minimization (2014)A new uncertainty importance measure (2006)
this work references cited by second hop

References

  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
  • 2010Verification and Validation in Scientific Computing1,551 cited
  • 2002Error and uncertainty in modeling and simulation629 cited
  • 2015Chaospy: An open source tool for designing methods of uncertainty quantification336 cited
  • 2020EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing54 cited
  • 2016FabSim: Facilitating computational research through automation on large-scale and distributed e-infrastructures38 cited

Cited by

  • 2021Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit28 cited
  • 2021When we can trust computers (and when we can't)26 cited
  • 2021Uncertainty quantification patterns for multiscale models19 cited
  • 2026Uncertainty Quantification for In Silico Chemistry13 cited

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