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Sparse Bayesian learning for data driven polynomial chaos expansion with application to chemical processes
16 references·0 citing·45 second hop
Sparse Bayesian learning for data driven polynomial chaos expansion with application to chemical processes (2018)Sparse Bayesian learning for data driven …Robust optimization for decision-making under endogenous uncertainty (2018)Robust optimization for decis…Global Sensitivity Analysis and Uncertainty Quantification of Crude Distillation Unit Using Surrogate Model Based on Gaussian Process Regression (2018)Global Sensitivity Analysis a…Data-driven robust optimization based on kernel learning (2017)Data-driven robust optimizati…Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach (2016)Uncertainty quantification an…SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos (2016)SAMBA: Sparse Approximation o…Polynomial chaos‐based robust design of systems with probabilistic uncertainties (2016)Polynomial chaos‐based robust…POLYNOMIAL-CHAOS-BASED KRIGING (2015)POLYNOMIAL-CHAOS-BASED KRIGINGData-driven individual and joint chance-constrained optimization via kernel smoothing (2015)Data-driven individual and jo…Reliability of complex chemical engineering processes (2014)Reliability of complex chemic…Process data analytics in the era of big data (2014)Process data analytics in the…An overview of methods to evaluate uncertainty of deterministic models in decision support (2014)An overview of methods to eva…Sensitivity study of dynamic systems using polynomial chaos (2012)Sensitivity study of dynamic …Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion (2012)Data-driven uncertainty quant…Theory and Applications of Robust Optimization (2010)Theory and Applications of Ro…Global Sensitivity Analysis. 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References

  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2010Theory and Applications of Robust Optimization2,515 cited
  • 2014An overview of methods to evaluate uncertainty of deterministic models in decision support539 cited
  • 2012Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion451 cited
  • 2015POLYNOMIAL-CHAOS-BASED KRIGING398 cited
  • 2014Process data analytics in the era of big data388 cited
  • 2017Data-driven robust optimization based on kernel learning217 cited
  • 2016SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos100 cited
  • 2015Data-driven individual and joint chance-constrained optimization via kernel smoothing73 cited
  • 2018Robust optimization for decision-making under endogenous uncertainty68 cited
  • 2012Sensitivity study of dynamic systems using polynomial chaos60 cited
  • 2016Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach39 cited
  • 2016Polynomial chaos‐based robust design of systems with probabilistic uncertainties35 cited
  • 2014Reliability of complex chemical engineering processes

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  • 2018Global Sensitivity Analysis and Uncertainty Quantification of Crude Distillation Unit Using Surrogate Model Based on Gaussian Process Regression22 cited
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