What is non-intrusive polynomial chaos method?
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The non-intrusive polynomial chaos method is a technique used for uncertainty quantification in complex systems without directly modifying the original simulation code. It involves constructing a surrogate model using polynomial chaos expansions to efficiently quantify uncertainties arising from input parameters. This method typically involves reducing the dimensionality of the high-dimensional space using techniques like Proper Orthogonal Decomposition (POD) and then mapping the uncertain input parameters to a low-dimensional latent space for prediction. By utilizing regression-based approaches like least squares polynomial chaos regression, this method can accurately capture the variability of output quantities of interest with limited sampling budgets. The non-intrusive polynomial chaos method is particularly useful for systems with nonlinear features and high computational costs, such as aeroelastic analyses in aircraft design.
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| Papers (5) | Insight |
|---|---|
1 Citations | Non-intrusive polynomial chaos method is Operator Inference (OpInf) in this study, inferring polynomial operators in reduced space for predicting chaotic dynamical systems without full order operator access. |
20 Jun 2022 1 Citations | Non-intrusive polynomial chaos method is utilized for nonlinear aeroelastic uncertainty quantification, offering direct analysis of limit cycle oscillation response curves, unlike traditional time-domain approaches. |
| Non-intrusive Polynomial Chaos Expansion (PCE) is a method for Uncertainty Quantification (UQ) in simulations, reducing computational effort by efficiently quantifying output variability from input uncertainties in complex systems. | |
19 Jan 2023 2 Citations | Non-intrusive polynomial chaos method in high-dimensional fields uses reduced order modeling to predict uncertain outputs with complex features, leveraging sparse regression-based polynomial chaos expansion for efficient uncertainty quantification. |
| Non-intrusive polynomial chaos method, specifically Least Squares Polynomial Chaos Regression (LSPCR), is proposed for stochastic analysis of transmission lines, efficiently determining PCE coefficients without directly solving the governing equations. |
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