Multilevel Adaptive Sparse Grid Quadrature for Monte Carlo models

Döpking, Sandra; Matera, Sebastian

doi:10.48550/arxiv.1810.00810

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Sensitivity Analysis and Uncertainty1

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2019

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“…Finally, we need a methodology to efficiently explore the usually high-dimensional parameter space. When the error distribution can be mapped on a hypercube, methods like Quasi Monte Carlo 16,293 or (multilevel) adaptive Sparse Grids 163,164,294 offer a good alternative to standard Monte Carlo because of their higher asymptotic convergence rates. Overall, we expect significant process in the near future on performing sensitivity analysis under uncertainty.…”

Section: Sensitivity Analysis and Uncertaintymentioning

confidence: 99%

Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis

et al. 2019

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Chemical kinetic modeling in heterogeneous catalysis is advancing in its ability to provide qualitatively or even quantitatively accurate prediction of real-world behavior because of new advances in the physical and chemical representations of catalytic systems, estimation of relevant kinetic parameters, and capabilities in kinetic modeling. This Perspective describes current trends and future areas of advancement in chemical kinetic modeling, simulation, and parameter estimation: ranging from elementary step calculations to multiscale modeling to the role of advanced statistical methods for incorporating uncertainties in predictions. Multiple new or growing methodologies are covered, examples are provided, and forward-looking topics for advancement are noted.

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