Results 11 to 20 of about 30,004 (201)
Physics-informed polynomial chaos expansions
Journal of Computational Physics, 2023 Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is beneficial to constrain the approximation to adhere to the known physics of the model.
Lukáš Novák +2 more
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Shock and Vibration, 2021 For the vibro-acoustic system with interval and random uncertainties, polynomial chaos expansions have received broad and persistent attention. Nevertheless, the cost of the computation process increases sharply with the increasing number of uncertain ...
Shengwen Yin, Xiaohan Zhu, Xiang Liu
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Probabilistic load margin assessment considering forecast error of wind power generation
Energy Reports, 2023 The increasing integration of wind power in power systems necessitates the probabilistic assessment of various uncertain factors. In operational planning, modeling short-term scale uncertainties, i.e., wind power forecast errors, plays an important role.
Chenxu Wang +3 more
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Polynomial chaos Kalman filter for target tracking applications
IET Radar, Sonar & Navigation, 2023 In this paper, an approximate Gaussian state estimator is developed based on generalised polynomial chaos expansion for target tracking applications. Motivated by the fact that calculating conditional moments in an approximate Gaussian filter involves ...
Kundan Kumar +3 more
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Frontiers in Physics, 2023 An efficient method of moments (MoM) based on polynomial chaos expansion (PCE) is applied to quickly calculate the electromagnetic scattering problems. The triangle basic functions are used to discretize the surface integral equations.
Xiaohui Yuan +5 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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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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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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