Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC

Julia Calatayud; Juan Carlos Cortés; Marc Jornet; Rafael Jacinto Villanueva

doi:10.1002/mma.5315

Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC

Mathematical Methods in the Applied Sciences ◽

10.1002/mma.5315 ◽

2018 ◽

Vol 41 (18) ◽

pp. 9618-9627 ◽

Cited By ~ 5

Author(s):

Julia Calatayud ◽

Juan Carlos Cortés ◽

Marc Jornet ◽

Rafael Jacinto Villanueva

Keyword(s):

Uncertainty Quantification ◽

Random Time ◽

Epidemiological Models ◽

Computational Uncertainty

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Adaptive finite difference solutions of Liouville equations in computational uncertainty quantification

Reliability Engineering & System Safety ◽

10.1016/j.ress.2015.05.024 ◽

2015 ◽

Vol 142 ◽

pp. 267-278 ◽

Cited By ~ 5

Author(s):

M. Razi ◽

P.J. Attar ◽

P. Vedula

Keyword(s):

Finite Difference ◽

Uncertainty Quantification ◽

Computational Uncertainty

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Review of Uncertainty Quantification of Measurement and Computational Modeling in EMC Part II: Computational Uncertainty

IEEE Transactions on Electromagnetic Compatibility ◽

10.1109/temc.2019.2904999 ◽

2019 ◽

Vol 61 (6) ◽

pp. 1699-1706 ◽

Cited By ~ 4

Author(s):

Sebastien Lallechere ◽

Carlo F. M. Carobbi ◽

Luk R. Arnaut

Keyword(s):

Computational Modeling ◽

Uncertainty Quantification ◽

Computational Uncertainty

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Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2016.02.016 ◽

2016 ◽

Vol 89 ◽

pp. 11-26 ◽

Cited By ~ 9

Author(s):

Andrea Barth ◽

Raimund Bürger ◽

Ilja Kröker ◽

Christian Rohde

Keyword(s):

Uncertainty Quantification ◽

Random Perturbations ◽

Galerkin Approach ◽

Computational Uncertainty ◽

Stochastic Galerkin

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Model Order Reduction Methods in Computational Uncertainty Quantification

Handbook of Uncertainty Quantification ◽

10.1007/978-3-319-11259-6_70-1 ◽

2015 ◽

pp. 1-53

Author(s):

Peng Chen ◽

Christoph Schwab

Keyword(s):

Uncertainty Quantification ◽

Model Order Reduction ◽

Order Reduction ◽

Model Order ◽

Reduction Methods ◽

Computational Uncertainty

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Uncertainty quantification in epidemiological models for the COVID-19 pandemic

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2020.104011 ◽

2020 ◽

Vol 125 ◽

pp. 104011

Author(s):

Leila Taghizadeh ◽

Ahmad Karimi ◽

Clemens Heitzinger

Keyword(s):

Uncertainty Quantification ◽

Epidemiological Models

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Model Order Reduction Methods in Computational Uncertainty Quantification

Handbook of Uncertainty Quantification ◽

10.1007/978-3-319-12385-1_70 ◽

2017 ◽

pp. 937-990

Author(s):

Peng Chen ◽

Christoph Schwab

Keyword(s):

Uncertainty Quantification ◽

Model Order Reduction ◽

Order Reduction ◽

Model Order ◽

Reduction Methods ◽

Computational Uncertainty

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Adaptive Numerical Solutions of Fokker-Planck Equations in Computational Uncertainty Quantification

52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference ◽

10.2514/6.2011-1976 ◽

2011 ◽

Author(s):

Mani Razi ◽

Peter Attar ◽

Prakash Vedula

Keyword(s):

Uncertainty Quantification ◽

Numerical Solutions ◽

Computational Uncertainty ◽

Fokker Planck Equations ◽

Fokker Planck

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Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration

Statistics and Computing ◽

10.1007/s11222-020-09926-w ◽

2020 ◽

Vol 30 (4) ◽

pp. 907-932 ◽

Cited By ~ 1

Author(s):

Assyr Abdulle ◽

Giacomo Garegnani

Keyword(s):

Uncertainty Quantification ◽

Numerical Integration ◽

Probabilistic Methods ◽

Random Time ◽

Geometric Numerical Integration ◽

Time Step

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Improved Efficiency of a Multi-Index FEM for Computational Uncertainty Quantification

SIAM Journal on Numerical Analysis ◽

10.1137/18m1193700 ◽

2019 ◽

Vol 57 (4) ◽

pp. 1744-1769

Author(s):

Josef Dick ◽

Michael Feischl ◽

Christoph Schwab

Keyword(s):

Uncertainty Quantification ◽

Multi Index ◽

Computational Uncertainty

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Computational uncertainty quantification for some strongly degenerate parabolic convection–diffusion equations

Journal of Computational and Applied Mathematics ◽

10.1016/j.cam.2018.09.006 ◽

2019 ◽

Vol 348 ◽

pp. 490-508 ◽

Cited By ~ 1

Author(s):

Raimund Bürger ◽

Ilja Kröker

Keyword(s):

Uncertainty Quantification ◽

Diffusion Equations ◽

Convection Diffusion ◽

Computational Uncertainty ◽

Degenerate Parabolic ◽

Strongly Degenerate

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