SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Facilitating Bayesian analysis of combustion kinetic models with artificial neural network
15 references·3 citing·40 second hop
Facilitating Bayesian analysis of combustion kinetic models with artificial neural network (2019)Facilitating Bayesian analysis of combust…Accelerate global sensitivity analysis using artificial neural network algorithm: Case studies for combustion kinetic model (2016)Accelerate global sensitivity…Chaospy: An open source tool for designing methods of uncertainty quantification (2015)Chaospy: An open source tool …Combustion kinetic model uncertainty quantification, propagation and minimization (2014)Combustion kinetic model unce…The method of uncertainty quantification and minimization using polynomial chaos expansions (2011)The method of uncertainty qua…Process informatics tools for predictive modeling: Hydrogen combustion (2011)Process informatics tools for…Uncertainty quantification in chemical systems (2009)Uncertainty quantification in…A global sensitivity study of sulfur chemistry in a premixed methane flame model using HDMR (2008)A global sensitivity study of…Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…Efficient Sampling for Non-Intrusive Polynomial Chaos Applications with Multiple Uncertain Input Variables (2007)Efficient Sampling for Non-In…Bayesian analysis of computer code outputs: A tutorial (2006)Bayesian analysis of computer…Uncertainty propagation using Wiener–Haar expansions (2004)Uncertainty propagation using…Spectral stochastic uncertainty quantification in chemical systems (2004)Spectral stochastic uncertain…Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection (2003)Uncertainty quantification in…Global uncertainty assessments by high dimensional model representations (HDMR) (2002)Global uncertainty assessment…High Dimensional Model Representations (2001)High Dimensional Model Repres…Uncertainty quantification of a deep learning fuel property prediction model (2023)Uncertainty quantification of…Neural network approach to response surface development for reaction model optimization and uncertainty minimization (2023)Neural network approach to re…OptEx: An integrated framework for experimental design and combustion kinetic model optimization (2022)OptEx: An integrated framewor…Stochastic Finite Elements: A Spectral Approach (1991)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)Modeling uncertainty in flow simulations via generalized polynomial chaos (2003)Multi-resolution analysis of Wiener-type uncertainty propagation schemes (2004)Efficient Collocational Approach for Parametric Uncertainty Analysis (2007)A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (2010)Global Sensitivity Analysis (1995)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)General formulation of HDMR component functions with independent and correlated variables (2011)Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017)Statistics for experimenters. An introduction to design, data analysis, and model building. (1978)On the Statistical Calibration of Physical Models (2015)Shared low-dimensional subspaces for propagating kinetic uncertainty to multiple outputs (2017)Efficient input—output model representations (1999)GUI–HDMR – A software tool for global sensitivity analysis of complex models (2008)An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations (2010)Global Sensitivity Analysis for Systems with Independent and/or Correlated Inputs (2010)Design and Analysis of Computer Experiments (1989)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Quantifying kinetic uncertainty in turbulent combustion simulations using active subspaces (2018)Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience (2018)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)A Stochastic Projection Method for Fluid Flow (2002)Point-Collocation Nonintrusive Polynomial Chaos Method for Stochastic Computational Fluid Dynamics (2010)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)The Design and Analysis of Computer Experiments (2003)Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)An overview of methods to evaluate uncertainty of deterministic models in decision support (2014)Polynomial Chaos in Stochastic Finite Elements (1990)Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes (2004)Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification (2009)An Introduction to Copulas (1999)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)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)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)
this work references cited by second hop

References

  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
  • 2006Bayesian analysis of computer code outputs: A tutorial756 cited
  • 2001High Dimensional Model Representations497 cited
  • 2004Uncertainty propagation using Wiener–Haar expansions438 cited
  • 2007Efficient Sampling for Non-Intrusive Polynomial Chaos Applications with Multiple Uncertain Input Variables347 cited
  • 2014Combustion kinetic model uncertainty quantification, propagation and minimization342 cited
  • 2015Chaospy: An open source tool for designing methods of uncertainty quantification336 cited
  • 2003Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection333 cited
  • 2011The method of uncertainty quantification and minimization using polynomial chaos expansions230 cited
  • 2002Global uncertainty assessments by high dimensional model representations (HDMR)179 cited
  • 2009Uncertainty quantification in chemical systems139 cited
  • 2008A global sensitivity study of sulfur chemistry in a premixed methane flame model using HDMR124 cited
  • 2004Spectral stochastic uncertainty quantification in chemical systems107 cited
  • 2016Accelerate global sensitivity analysis using artificial neural network algorithm: Case studies for combustion kinetic model101 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2011Process informatics tools for predictive modeling: Hydrogen combustion54 cited
  • Cited by

    • 2023Neural network approach to response surface development for reaction model optimization and uncertainty minimization41 cited
    • 2022OptEx: An integrated framework for experimental design and combustion kinetic model optimization27 cited
    • 2023Uncertainty quantification of a deep learning fuel property prediction model12 cited