Data-driven polynomial chaos expansions: A weighted least-square approximation

Journal of Computational Physics ◽

10.1016/j.jcp.2018.12.020 ◽

2019 ◽

Vol 381 ◽

pp. 129-145 ◽

Author(s):

Ling Guo ◽

Yongle Liu ◽

Tao Zhou

Keyword(s):

Polynomial Chaos ◽

Least Square ◽

Data Driven ◽

Weighted Least Square ◽

Polynomial Chaos Expansions

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Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square

Computer Methods in Applied Mechanics and Engineering ◽

10.1016/j.cma.2020.112906 ◽

2020 ◽

Vol 364 ◽

pp. 112906

Author(s):

Yicheng Zhou ◽

Zhenzhou Lu ◽

Jinghan Hu ◽

Yingshi Hu

Keyword(s):

Polynomial Chaos ◽

Surrogate Modeling ◽

Partial Least Square ◽

Least Square ◽

Data Driven ◽

High Dimensional ◽

Polynomial Chaos Expansions

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Data-driven sensitivity indices for models with dependent inputs using polynomial chaos expansions

Structural Safety ◽

10.1016/j.strusafe.2020.101984 ◽

2021 ◽

Vol 88 ◽

pp. 101984

Author(s):

Zhanlin Liu ◽

Youngjun Choe

Keyword(s):

Polynomial Chaos ◽

Data Driven ◽

Sensitivity Indices ◽

Polynomial Chaos Expansions

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Gradient-enhanced Least-square Polynomial Chaos Expansions for Uncertainty Quantification and Robust Optimization

AIAA AVIATION 2021 FORUM ◽

10.2514/6.2021-3073 ◽

2021 ◽

Author(s):

Tiziano Ghisu ◽

Diego I. Lopez ◽

Pranay Seshadri ◽

Shahrokh Shahpar

Keyword(s):

Robust Optimization ◽

Uncertainty Quantification ◽

Polynomial Chaos ◽

Least Square ◽

Polynomial Chaos Expansions

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

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.107923 ◽

2021 ◽

pp. 107923

Author(s):

Nabil El Moçayd ◽

Mohammed Seaid

Keyword(s):

Polynomial Chaos ◽

Data Driven ◽

Complex Fluid ◽

Polynomial Chaos Expansions ◽

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Sparse Approximation of Data-Driven Polynomial Chaos Expansions: An Induced Sampling Approach

Communications in Mathematical Research ◽

10.4208/cmr.2020-0010 ◽

2020 ◽

Vol 36 (2) ◽

pp. 128-153 ◽

Author(s):

Ling Guo

Keyword(s):

Polynomial Chaos ◽

Sparse Approximation ◽

Data Driven ◽

Polynomial Chaos Expansions ◽

Sampling Approach

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Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations

Computational Geosciences ◽

10.1007/s10596-013-9350-6 ◽

2013 ◽

Vol 17 (4) ◽

pp. 671-687 ◽

Author(s):

Sergey Oladyshkin ◽

Holger Class ◽

Wolfgang Nowak

Keyword(s):

Carbon Dioxide ◽

History Matching ◽

Polynomial Chaos ◽

Bayesian Updating ◽

Data Driven ◽

Carbon Dioxide Storage ◽

Polynomial Chaos Expansions ◽

Geological Formations

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SURROGATE MODELING FOR STOCHASTIC DYNAMICAL SYSTEMS BY COMBINING NONLINEAR AUTOREGRESSIVE WITH EXOGENOUS INPUT MODELS AND POLYNOMIAL CHAOS EXPANSIONS

International Journal for Uncertainty Quantification ◽

10.1615/int.j.uncertaintyquantification.2016016603 ◽

2016 ◽

Vol 6 (4) ◽

pp. 313-339 ◽

Author(s):

Chu V. Mai ◽

Minas D. Spiridonakos ◽

Eleni N. Chatzi ◽

Bruno Sudret

Keyword(s):

Dynamical Systems ◽

Polynomial Chaos ◽

Surrogate Modeling ◽

Stochastic Dynamical Systems ◽

Polynomial Chaos Expansions ◽

Nonlinear Autoregressive

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A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables

IEEE Transactions on Power Systems ◽

10.1109/tpwrs.2020.3034520 ◽

2020 ◽

pp. 1-1

Author(s):

Xiaoting Wang ◽

Xiaozhe Wang ◽

Hao Sheng ◽

Xi Lin

Keyword(s):

Power Systems ◽

Polynomial Chaos ◽

Expansion Method ◽

Polynomial Chaos Expansion ◽

Data Driven ◽

Chaos Expansion ◽

Total Transfer Capability ◽

Sparse Polynomial ◽

Transfer Capability ◽

Sparse Polynomial Chaos Expansion

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Extending bluff-and-fix estimates for polynomial chaos expansions

Journal of Computational Science ◽

10.1016/j.jocs.2020.101287 ◽

2021 ◽

pp. 101287

Author(s):

Laura Lyman ◽

Gianluca Iaccarino

Keyword(s):

Polynomial Chaos ◽

Polynomial Chaos Expansions

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Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2021.107853 ◽

2021 ◽

Vol 160 ◽

pp. 107853

Author(s):

M.M. Manjurul Islam ◽

Alexander E. Prosvirin ◽

Jong-Myon Kim

Keyword(s):

Support Vector Machines ◽

Least Square ◽

Data Driven ◽

Support Vector ◽

Rolling Element Bearings ◽

Health Index ◽

Vector Machines ◽

Rolling Element

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