Sparse polynomial chaos expansions: Literature survey and benchmark
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 …
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
Compressive sensing adaptation for polynomial chaos expansions
Abstract Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the
underlying Gaussian germ. Several rotations have been proposed in the literature resulting …
underlying Gaussian germ. Several rotations have been proposed in the literature resulting …
[HTML][HTML] A scalable adaptive sampling approach for surrogate modeling of rigid pavements using machine learning
Rigid pavement design is a high-dimensional optimization problem, involving several
variables and design considerations. The existing machine learning (ML) design models are …
variables and design considerations. The existing machine learning (ML) design models are …
[HTML][HTML] A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design
BY Zhang, YQ Ni - Computer Methods in Applied Mechanics and …, 2021 - Elsevier
Abstract Sparse representation of Polynomial Chaos Expansion (PCE) has been widely
used in the field of Uncertainty Quantification (UQ) due to its simple model structure and low …
used in the field of Uncertainty Quantification (UQ) due to its simple model structure and low …
An ensemble Synthetic Eddy Method for accurate treatment of inhomogeneous turbulence
KA Schau, C Johnson, J Muller, JC Oefelein - Computers & Fluids, 2022 - Elsevier
An ensemble approach to generating turbulent inflow boundary conditions using the
Synthetic Eddy Method is proposed that improves signal accuracy in recovering target …
Synthetic Eddy Method is proposed that improves signal accuracy in recovering target …
Global sensitivity analysis and estimation of model error, toward uncertainty quantification in scramjet computations
The development of scramjet engines is an important research area for advancing
hypersonic and orbital flights. Progress toward optimal engine designs requires accurate …
hypersonic and orbital flights. Progress toward optimal engine designs requires accurate …
An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework
Y Zhou, Z Lu, K Cheng, C Ling - Computer Methods in Applied Mechanics …, 2019 - Elsevier
Sparse polynomial chaos expansion has been widely used to tackle problems of function
approximation in the field of uncertain quantification. The accuracy of PCE depends on how …
approximation in the field of uncertain quantification. The accuracy of PCE depends on how …
Sparse Polynomial Chaos expansions using variational relevance vector machines
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the
computational efficiency and accuracy under a limited number of model simulations. These …
computational efficiency and accuracy under a limited number of model simulations. These …
Uncertainty quantification and reliability analysis by an adaptive sparse Bayesian inference based PCE model
B Bhattacharyya - Engineering with Computers, 2022 - Springer
An adaptive Bayesian polynomial chaos expansion (BPCE) is developed in this paper for
uncertainty quantification (UQ) and reliability analysis. The sparsity in the PCE model is …
uncertainty quantification (UQ) and reliability analysis. The sparsity in the PCE model is …
Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters
Uncertainty quantification (UQ) and inference involving a large number of parameters are
valuable tools for problems associated with heterogeneous and non-stationary behaviors …
valuable tools for problems associated with heterogeneous and non-stationary behaviors …