Hierarchical adaptive polynomial chaos expansions [PDF]
Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2015), 2015 Polynomial chaos expansions (PCE) are widely used in the framework of uncertainty quantification. However, when dealing with high dimensional complex problems, challenging issues need to be faced.
Mai, Chu V., Sudret, Bruno
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Uncertainty Propagation and Global Sensitivity Analysis of a Surface Acoustic Wave Gas Sensor Using Finite Elements and Sparse Polynomial Chaos Expansions [PDF]
Vibration, 2023 The aim of this work is to perform an uncertainty propagation and global sensitivity analysis of a surface acoustic wave (SAW) gas sensor using finite elements and sparse polynomial chaos.
Mohamed Hamdaoui
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Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions. [PDF]
PLoS Computational Biology, 2019 We present a novel surrogate modeling method that can be used to accelerate the solution of uncertainty quantification (UQ) problems arising in nonlinear and non-smooth models of biological systems.
Joel A Paulson +2 more
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Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2021 Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful sparse regression solvers to approximate computer models with many input parameters, relying on only few model evaluations.
Lüthen, Nora +2 more
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Fast Stochastic Surrogate Modeling via Rational Polynomial Chaos Expansions and Principal Component Analysis [PDF]
IEEE Access, 2021 This paper introduces a fast stochastic surrogate modeling technique for the frequency-domain responses of linear and passive electrical and electromagnetic systems based on polynomial chaos expansion (PCE) and principal component analysis (PCA).
Paolo Manfredi, Stefano Grivet-Talocia
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Stochastic Chaos and Markov Blankets [PDF]
Entropy, 2021 In this treatment of random dynamical systems, we consider the existence—and identification—of conditional independencies at nonequilibrium steady-state.
Karl Friston +4 more
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Uncertainty propagation in pore water chemical composition calculation using surrogate models [PDF]
Scientific Reports, 2022 Performance assessment in deep geological nuclear waste repository systems necessitates an extended knowledge of the pore water chemical conditions prevailing in host-rock formations.
Pierre Sochala +3 more
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Projection pursuit adaptation on polynomial chaos expansions
Computer Methods in Applied Mechanics and Engineering, 2022 The present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities
Xiaoshu Zeng, Roger Ghanem
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Broad ranges of investment configurations for renewable power systems, robust to cost uncertainty and near-optimality [PDF]
iScience, 2023 Summary: Achieving ambitious CO2 emission reduction targets requires energy system planning to accommodate societal preferences, such as transmission reinforcements or onshore wind parks, and acknowledge uncertainties in technology cost projections among
Fabian Neumann, Tom Brown
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A Posteriori Validation of Generalized Polynomial Chaos Expansions
SIAM Journal on Applied Dynamical Systems, 2023 Generalized polynomial chaos expansions are a powerful tool to study differential equations with random coefficients, allowing in particular to efficiently approximate random invariant sets associated to such equations. In this work, we use ideas from validated numerics in order to obtain rigorous a posteriori error estimates together with existence ...
Maxime Breden
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