Results 21 to 30 of about 31,846 (223)
Data-driven sparse polynomial chaos expansion for models with dependent inputs
Journal of Safety Science and Resilience, 2023 Polynomial chaos expansions (PCEs) have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of the inputs.
Zhanlin Liu, Youngjun Choe
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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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Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification [PDF]
Reliability Engineering & System Safety, 2021 The surrogate model-based uncertainty quantification method has drawn much attention in many engineering fields. Polynomial chaos expansion (PCE) and deep learning (DL) are powerful methods for building a surrogate model.
W. Yao +4 more
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Mechanics & Industry, 2023 Surface response models, such as polynomial chaos Expansion, are commonly used to deal with the case of uncertain input parameters. Such models are only surrogates, so it is necessary to develop tools to assess the level of error between the reference ...
Serra Quentin, Florentin Eric
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Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines [PDF]
Wind Energy Science, 2019 The present paper characterizes the performance of non-intrusive uncertainty quantification methods for aeroservoelastic wind turbine analysis. Two different methods are considered, namely non-intrusive polynomial chaos expansion and Kriging.
P. Bortolotti +4 more
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Polynomial chaos expansion for sensitivity analysis of model output with dependent inputs
Reliability Engineering & System Safety, 2021 In this paper, we discuss the sensitivity analysis of model response when the uncertain model inputs are not independent of one other. In this case, two different kinds of sensitivity indices can be evaluated: (i) the sensitivity indices that account for
T. Mara, W. Becker
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From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases [PDF]
Wind Energy Science, 2018 We define and demonstrate a procedure for quick assessment of site-specific lifetime fatigue loads using simplified load mapping functions (surrogate models), trained by means of a database with high-fidelity load simulations.
N. Dimitrov +3 more
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STOCHASTIC POLYNOMIAL CHAOS EXPANSIONS TO EMULATE STOCHASTIC SIMULATORS
International Journal for Uncertainty Quantification, 2023 In the context of uncertainty quantification, computational models are required to be repeatedly evaluated. This task is intractable for costly numerical models. Such a problem turns out to be even more severe for stochastic simulators, the output of which is a random variable for a given set of input parameters.
Zhu, Xujia +1 more
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An Efficient Polynomial Chaos Method for Stiffness Analysis of Air Spring Considering Uncertainties
Complexity, 2021 Traditional methods for stiffness analysis of the air spring are based on deterministic assumption that the parameters are fixed. However, uncertainties have widely existed, and the mechanic property of the air spring is very sensitive to these ...
Feng Kong, Penghao Si, Shengwen Yin
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Polynomial chaos expansions for damped oscillators
, 2015 Uncertainty quantification is the state-of-the-art framework dealing with uncertainties arising in all kind of real-life problems. One of the framework’s functions is to propagate uncertainties from the stochastic input factors to the output quantities of interest, hence the name uncertainty propagation.
Mai, Chu V., Sudret, Bruno
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