Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

A polymorphic uncertainty model for the curing process of transversely fiber-reinforced plastics

Eduard Penner, Ismail Caylak and Rolf Mahnken
Mathematics and Mechanics of Complex Systems 10 (1) 21 (2022)
https://doi.org/10.2140/memocs.2022.10.21

A Homotopy Bayesian Approach for Inverse Problems

Xiao-Mei Yang, Zhi-Liang Deng and Harendra Singh
Mathematical Problems in Engineering 2022 1 (2022)
https://doi.org/10.1155/2022/9680613

Uncertainty propagation in pore water chemical composition calculation using surrogate models

Pierre Sochala, Christophe Chiaberge, Francis Claret and Christophe Tournassat
Scientific Reports 12 (1) (2022)
https://doi.org/10.1038/s41598-022-18411-5

Variational inference with NoFAS: Normalizing flow with adaptive surrogate for computationally expensive models

Yu Wang, Fang Liu and Daniele E. Schiavazzi
Journal of Computational Physics 467 111454 (2022)
https://doi.org/10.1016/j.jcp.2022.111454

Nonlinear Vibrations of Simply Supported Cylindrical Panels with Uncertain Parameters: An Intrusive Application of the Generalized Polynomial Chaos Expansion

Anna Elizabete F. Palla and Frederico M. A. Silva
Journal of Vibration Engineering & Technologies 10 (8) 2917 (2022)
https://doi.org/10.1007/s42417-022-00527-7

Convergence Analysis and Adaptive Order Selection for the Polynomial Chaos Approach to Direct Optimal Control under Uncertainties

Lilli Frison and Christian Kirches
SIAM Journal on Control and Optimization 59 (1) 509 (2021)
https://doi.org/10.1137/17M1133038

A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

Nikolaos Tsokanas, Xujia Zhu, Giuseppe Abbiati, Stefano Marelli, Bruno Sudret and Božidar Stojadinović
Frontiers in Built Environment 7 (2021)
https://doi.org/10.3389/fbuil.2021.778716

Modeling of Allee effect in biofilm formation via the stochastic bistable Allen–Cahn partial differential equation

Marc Jornet
Stochastic Analysis and Applications 39 (1) 22 (2021)
https://doi.org/10.1080/07362994.2020.1777163

Modified Dimension Reduction-Based Polynomial Chaos Expansion for Nonstandard Uncertainty Propagation and Its Application in Reliability Analysis

Jeongeun Son and Yuncheng Du
Processes 9 (10) 1856 (2021)
https://doi.org/10.3390/pr9101856

A note on stochastic polynomial chaos expansions for uncertain volatility and Asian option pricing

Y.-T. Lin, Y.-T. Shih, C.-S. Chien and Q. Sheng
Applied Mathematics and Computation 393 125764 (2021)
https://doi.org/10.1016/j.amc.2020.125764

Sensitivity analysis of random linear differential–algebraic equations using system norms

Roland Pulch, Akil Narayan and Tatjana Stykel
Journal of Computational and Applied Mathematics 397 113666 (2021)
https://doi.org/10.1016/j.cam.2021.113666

Non-intrusive polynomial chaos methods for uncertainty quantification in wave problems at high frequencies

Nabil El Mocayd, M Shadi Mohamed and Mohammed Seaid
Journal of Computational Science 53 101344 (2021)
https://doi.org/10.1016/j.jocs.2021.101344

A Convex Optimization Framework for the Inverse Problem of Identifying a Random Parameter in a Stochastic Partial Differential Equation

Baasansuren Jadamba, Akhtar A. Khan, Miguel Sama, Hans-Jorg Starkloff and Christiane Tammer
SIAM/ASA Journal on Uncertainty Quantification 9 (2) 922 (2021)
https://doi.org/10.1137/20M1323953

Uncertainty quantification for random Hamiltonian systems by using polynomial expansions and geometric integrators

Marc Jornet
Chaos, Solitons & Fractals 151 111208 (2021)
https://doi.org/10.1016/j.chaos.2021.111208

Data-driven polynomial chaos expansions for characterization of complex fluid rheology: Case study of phosphate slurry

Nabil El Moçayd and Mohammed Seaid
Reliability Engineering & System Safety 216 107923 (2021)
https://doi.org/10.1016/j.ress.2021.107923

Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models

Xujia Zhu and Bruno Sudret
Reliability Engineering & System Safety 214 107815 (2021)
https://doi.org/10.1016/j.ress.2021.107815

Development of hybrid dimension adaptive sparse HDMR for stochastic finite element analysis of composite plate

Amit Kumar Rathi and Arunasis Chakraborty
Composite Structures 255 112915 (2021)
https://doi.org/10.1016/j.compstruct.2020.112915

Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion

Xiang Sun, Xiaomin Pan and Jung-Il Choi
Journal of Computational and Applied Mathematics 390 113372 (2021)
https://doi.org/10.1016/j.cam.2020.113372

Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems

Hugo Esquivel, Arun Prakash and Guang Lin
Journal of Computational and Applied Mathematics 398 113674 (2021)
https://doi.org/10.1016/j.cam.2021.113674

Emulation of Stochastic Simulators Using Generalized Lambda Models

Xujia Zhu and Bruno Sudret
SIAM/ASA Journal on Uncertainty Quantification 9 (4) 1345 (2021)
https://doi.org/10.1137/20M1337302

Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark

Nora Lüthen, Stefano Marelli and Bruno Sudret
SIAM/ASA Journal on Uncertainty Quantification 9 (2) 593 (2021)
https://doi.org/10.1137/20M1315774

A local hybrid surrogate‐based finite element tearing interconnecting dual‐primal method for nonsmooth random partial differential equations

Martin Eigel and Robert Gruhlke
International Journal for Numerical Methods in Engineering 122 (4) 1001 (2021)
https://doi.org/10.1002/nme.6571

Stability analysis of a hyperbolic stochastic Galerkin formulation for the Aw-Rascle-Zhang model with relaxation

Stephan Gerster, Michael Herty and Elisa Iacomini
Mathematical Biosciences and Engineering 18 (4) 4372 (2021)
https://doi.org/10.3934/mbe.2021220

Hyperbolicity-Preserving and Well-Balanced Stochastic Galerkin Method for Shallow Water Equations

Dihan Dai, Yekaterina Epshteyn and Akil Narayan
SIAM Journal on Scientific Computing 43 (2) A929 (2021)
https://doi.org/10.1137/20M1360736

Uncertainty quantification for the random viscous Burgers’ partial differential equation by using the differential transform method

Marc Jornet
Nonlinear Analysis 209 112340 (2021)
https://doi.org/10.1016/j.na.2021.112340

Conformally mapped polynomial chaos expansions for Maxwell's source problem with random input data

Niklas Georg and Ulrich Römer
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 33 (6) (2020)
https://doi.org/10.1002/jnm.2776

Adaptive stochastic Galerkin FEM for lognormal coefficients in hierarchical tensor representations

Martin Eigel, Manuel Marschall, Max Pfeffer and Reinhold Schneider
Numerische Mathematik 145 (3) 655 (2020)
https://doi.org/10.1007/s00211-020-01123-1

An Adaptive Stochastic Galerkin Tensor Train Discretization for Randomly Perturbed Domains

Martin Eigel, Manuel Marschall and Michael Multerer
SIAM/ASA Journal on Uncertainty Quantification 8 (3) 1189 (2020)
https://doi.org/10.1137/19M1246080

Improvement of random coefficient differential models of growth of anaerobic photosynthetic bacteria by combining Bayesian inference and gPC

Julia Calatayud, Juan Carlos Cortés and Marc Jornet
Mathematical Methods in the Applied Sciences 43 (14) 7885 (2020)
https://doi.org/10.1002/mma.5546

Compressed Principal Component Analysis of Non-Gaussian Vectors

Marc Mignolet and Christian Soize
SIAM/ASA Journal on Uncertainty Quantification 8 (4) 1261 (2020)
https://doi.org/10.1137/20M1322029

Sensitivity Analysis of Burgers' Equation with Shocks

Qin Li, Jian-Guo Liu and Ruiwen Shu
SIAM/ASA Journal on Uncertainty Quantification 8 (4) 1493 (2020)
https://doi.org/10.1137/18M1211763

Reservoir Computing Universality With Stochastic Inputs

Lukas Gonon and Juan-Pablo Ortega
IEEE Transactions on Neural Networks and Learning Systems 31 (1) 100 (2020)
https://doi.org/10.1109/TNNLS.2019.2899649

Computing Invariant Sets of Random Differential Equations Using Polynomial Chaos

Maxime Breden and Christian Kuehn
SIAM Journal on Applied Dynamical Systems 19 (1) 577 (2020)
https://doi.org/10.1137/18M1235818

Beyond the hypothesis of boundedness for the random coefficient of Airy, Hermite and Laguerre differential equations with uncertainties

Julia Calatayud Gregori, Juan-Carlos Cortés and Marc Jornet Sanz
Stochastic Analysis and Applications 38 (5) 875 (2020)
https://doi.org/10.1080/07362994.2020.1733017

Uncertainty quantification in a hydrogen production system based on the solar hybrid sulfur process

M. Venturin, L. Turchetti and R. Liberatore
International Journal of Hydrogen Energy 45 (29) 14679 (2020)
https://doi.org/10.1016/j.ijhydene.2020.03.200

Current Trends in Dynamical Systems in Biology and Natural Sciences

Francesco Florian and Rossana Vermiglio
SEMA SIMAI Springer Series, Current Trends in Dynamical Systems in Biology and Natural Sciences 21 205 (2020)
https://doi.org/10.1007/978-3-030-41120-6_11

On the Construction of Uncertain Time Series Surrogates Using Polynomial Chaos and Gaussian Processes

Pierre Sochala and Mohamed Iskandarani
Mathematical Geosciences 52 (2) 285 (2020)
https://doi.org/10.1007/s11004-019-09806-8

Uncertainty Propagation Using Polynomial Chaos Expansions for Extreme Sea Level Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis

Cléa Denamiel, Xun Huan, Jadranka Šepić and Ivica Vilibić
Journal of Physical Oceanography 50 (4) 1005 (2020)
https://doi.org/10.1175/JPO-D-19-0147.1

Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability

Alexander Litvinenko, Dmitry Logashenko, Raul Tempone, Gabriel Wittum and David Keyes
GEM - International Journal on Geomathematics 11 (1) (2020)
https://doi.org/10.1007/s13137-020-0147-1

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

Sharmila Karumuri, Rohit Tripathy, Ilias Bilionis and Jitesh Panchal
Journal of Computational Physics 404 109120 (2020)
https://doi.org/10.1016/j.jcp.2019.109120

Parseval inequalities and lower bounds for variance-based sensitivity indices

Olivier Roustant, Fabrice Gamboa and Bertrand Iooss
Electronic Journal of Statistics 14 (1) (2020)
https://doi.org/10.1214/19-EJS1673

Robust topology optimization for heat conduction with polynomial chaos expansion

André Jacomel Torii, Diogo Pereira da Silva Santos and Eduardo Morais de Medeiros
Journal of the Brazilian Society of Mechanical Sciences and Engineering 42 (6) (2020)
https://doi.org/10.1007/s40430-020-02367-6

Increasing wind farm efficiency by yaw control: beyond ideal studies towards a realistic assessment

U Ciri, M A Rotea and S Leonardi
Journal of Physics: Conference Series 1618 (2) 022029 (2020)
https://doi.org/10.1088/1742-6596/1618/2/022029

Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model

Julia Calatayud, Juan Carlos Cortés and Marc Jornet
Chaos, Solitons & Fractals 133 109639 (2020)
https://doi.org/10.1016/j.chaos.2020.109639

A hyperbolicity-preserving discontinuous stochastic Galerkin scheme for uncertain hyperbolic systems of equations

Jakob Dürrwächter, Thomas Kuhn, Fabian Meyer, Louisa Schlachter and Florian Schneider
Journal of Computational and Applied Mathematics 370 112602 (2020)
https://doi.org/10.1016/j.cam.2019.112602

Reliability-Based Optimization for Energy Refurbishment of a Social Housing Building

Marco Manzan, Giorgio Lupato, Amedeo Pezzi, Paolo Rosato and Alberto Clarich
Energies 13 (9) 2310 (2020)
https://doi.org/10.3390/en13092310

A posteriori error estimation and adaptivity in stochastic Galerkin FEM for parametric elliptic PDEs: Beyond the affine case

Alex Bespalov and Feng Xu
Computers & Mathematics with Applications 80 (5) 1084 (2020)
https://doi.org/10.1016/j.camwa.2020.05.023

Numerical approximation of poroelasticity with random coefficients using Polynomial Chaos and Hybrid High-Order methods

Michele Botti, Daniele A. Di Pietro, Olivier Le Maître and Pierre Sochala
Computer Methods in Applied Mechanics and Engineering 361 112736 (2020)
https://doi.org/10.1016/j.cma.2019.112736

Optimal Bayesian experimental design for subsurface flow problems

Alexadner Tarakanov and Ahmed H. Elsheikh
Computer Methods in Applied Mechanics and Engineering 370 113208 (2020)
https://doi.org/10.1016/j.cma.2020.113208

A two-stage surrogate model for Neo-Hookean problems based on adaptive proper orthogonal decomposition and hierarchical tensor approximation

Steffen Kastian, Dieter Moser, Lars Grasedyck and Stefanie Reese
Computer Methods in Applied Mechanics and Engineering 372 113368 (2020)
https://doi.org/10.1016/j.cma.2020.113368

Sparse polynomial surrogates for non-intrusive, high-dimensional uncertainty quantification of aeroelastic computations

Éric Savin and Jean-Luc Hantrais-Gervois
Probabilistic Engineering Mechanics 59 103027 (2020)
https://doi.org/10.1016/j.probengmech.2020.103027

Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation

Gaël Poëtte
Mathematics and Computers in Simulation 177 24 (2020)
https://doi.org/10.1016/j.matcom.2020.04.009

Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition

Nabil El Moçayd, M. Shadi Mohamed, Driss Ouazar and Mohammed Seaid
Reliability Engineering & System Safety 195 106733 (2020)
https://doi.org/10.1016/j.ress.2019.106733

Mathematical modeling of adulthood obesity epidemic in Spain using deterministic, frequentist and Bayesian approaches

Julia Calatayud and Marc Jornet
Chaos, Solitons & Fractals 140 110179 (2020)
https://doi.org/10.1016/j.chaos.2020.110179

Uncertainty Management for Robust Industrial Design in Aeronautics

Chris Lacor and Éric Savin
Notes on Numerical Fluid Mechanics and Multidisciplinary Design, Uncertainty Management for Robust Industrial Design in Aeronautics 140 687 (2019)
https://doi.org/10.1007/978-3-319-77767-2_42

An efficient method for stochastic optimal control with joint chance constraints for nonlinear systems

Joel A. Paulson and Ali Mesbah
International Journal of Robust and Nonlinear Control 29 (15) 5017 (2019)
https://doi.org/10.1002/rnc.3999

Optimization with constraints considering polymorphic uncertainties

Markus Mäck, Ismail Caylak, Philipp Edler, Steffen Freitag, Michael Hanss, Rolf Mahnken, Günther Meschke and Eduard Penner
GAMM-Mitteilungen 42 (1) (2019)
https://doi.org/10.1002/gamm.201900005

Some greedy algorithms for sparse polynomial chaos expansions

Ricardo Baptista, Valentin Stolbunov and Prasanth B. Nair
Journal of Computational Physics 387 303 (2019)
https://doi.org/10.1016/j.jcp.2019.01.035

Data-driven uncertainty quantification for Formula 1: Diffuser, wing tip and front wing variations

Richard Ahlfeld, Fabio Ciampoli, Marco Pietropaoli, Nick Pepper and Francesco Montomoli
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233 (6) 1495 (2019)
https://doi.org/10.1177/0954407019835315

Compressive Sensing Based Stochastic Economic Dispatch With High Penetration Renewables

Jing Li, Na Ou, Guang Lin and Wei Wei
IEEE Transactions on Power Systems 34 (2) 1438 (2019)
https://doi.org/10.1109/TPWRS.2018.2874718

Asymptotic expansion for some local volatility models arising in finance

Sergio Albeverio, Francesco Cordoni, Luca Di Persio and Gregorio Pellegrini
Decisions in Economics and Finance 42 (2) 527 (2019)
https://doi.org/10.1007/s10203-019-00247-w

Uncertainty quantification methodology for hyperbolic systems with application to blood flow in arteries

M. Petrella, S. Tokareva and E.F. Toro
Journal of Computational Physics 386 405 (2019)
https://doi.org/10.1016/j.jcp.2019.02.013

A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas

Emiliano Torre, Stefano Marelli, Paul Embrechts and Bruno Sudret
Probabilistic Engineering Mechanics 55 1 (2019)
https://doi.org/10.1016/j.probengmech.2018.08.001

Uncertainty quantification for nonlinear difference equations with dependent random inputs via a stochastic Galerkin projection technique

J. Calatayud, J.-C. Cortés and M. Jornet
Communications in Nonlinear Science and Numerical Simulation 72 108 (2019)
https://doi.org/10.1016/j.cnsns.2018.12.011

A second order SAP algorithm for risk and reliability based design optimization

A.J. Torii, R.H. Lopez and L.F.F. Miguel
Reliability Engineering & System Safety 190 106499 (2019)
https://doi.org/10.1016/j.ress.2019.106499

Predicting reinforcing bar development length using polynomial chaos expansions

Zaher Mundher Yaseen, Behrooz Keshtegar, Hyeon-Jong Hwang and Moncef L. Nehdi
Engineering Structures 195 524 (2019)
https://doi.org/10.1016/j.engstruct.2019.06.012

Model order reduction for random nonlinear dynamical systems and low-dimensional representations for their quantities of interest

Roland Pulch
Mathematics and Computers in Simulation 166 76 (2019)
https://doi.org/10.1016/j.matcom.2019.01.016

Data-driven polynomial chaos expansion for machine learning regression

Emiliano Torre, Stefano Marelli, Paul Embrechts and Bruno Sudret
Journal of Computational Physics 388 601 (2019)
https://doi.org/10.1016/j.jcp.2019.03.039

Polynomial chaos expansions for dependent random variables

John D. Jakeman, Fabian Franzelin, Akil Narayan, Michael Eldred and Dirk Plfüger
Computer Methods in Applied Mechanics and Engineering 351 643 (2019)
https://doi.org/10.1016/j.cma.2019.03.049

Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts

Justin S. Tran, Daniele E. Schiavazzi, Andrew M. Kahn and Alison L. Marsden
Computer Methods in Applied Mechanics and Engineering 345 402 (2019)
https://doi.org/10.1016/j.cma.2018.10.024

Stability Preservation in Stochastic Galerkin Projections of Dynamical Systems

Roland Pulch and Florian Augustin
SIAM/ASA Journal on Uncertainty Quantification 7 (2) 634 (2019)
https://doi.org/10.1137/17M1142223

Improving the Approximation of the First- and Second-Order Statistics of the Response Stochastic Process to the Random Legendre Differential Equation

J. Calatayud, J.-C. Cortés and M. Jornet
Mediterranean Journal of Mathematics 16 (3) (2019)
https://doi.org/10.1007/s00009-019-1338-6

Combining Polynomial Chaos Expansions and the Random Variable Transformation Technique to Approximate the Density Function of Stochastic Problems, Including Some Epidemiological Models

Julia Calatayud Gregori, Benito M. Chen-Charpentier, Juan Carlos Cortés López and Marc Jornet Sanz
Symmetry 11 (1) 43 (2019)
https://doi.org/10.3390/sym11010043

Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines

M. Carnevale and R. Ahlfeld
Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines 67 (2019)
https://doi.org/10.1007/978-3-319-92943-9_3

A hybrid stochastic domain decomposition method for partial differential equations with localised possibly rough random data

Robert Gruhlke, Martin Eigel, Dietmar Hömberg, Martin Drieschner and Yuri Petryna
PAMM 18 (1) (2018)
https://doi.org/10.1002/pamm.201800434

Numerical Procedures for Random Differential Equations

Mohamed Ben Said, Lahcen Azrar and Driss Sarsri
Journal of Applied Mathematics 2018 1 (2018)
https://doi.org/10.1155/2018/7403745

Robust Information Divergences for Model-Form Uncertainty Arising from Sparse Data in Random PDE

Eric Joseph Hall and Markos A. Katsoulakis
SIAM/ASA Journal on Uncertainty Quantification 6 (4) 1364 (2018)
https://doi.org/10.1137/17M1143344