Results 21 to 30 of about 303 (161)

A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions [PDF]

SIAM Journal on Scientific Computing, 2017
32 pages, 10 ...
John D. Jakeman, Akil Narayan 0001, Tao Zhou 0010   +2 more
openaire   +2 more sources

Machine Learning and Uncertainty Quantification for Surrogate Models of Integrated Devices With a Large Number of Parameters

IEEE Access, 2019
This paper deals with the application of the support vector machine (SVM) and the least-squares SVM regressions to the uncertainty quantification of complex systems with a high-dimensional parameter space.
Riccardo Trinchero, Mourad Larbi, Hakki M. Torun, Flavio G. Canavero, Madhavan Swaminathan   +4 more
doaj   +1 more source

Uncertainty Quantification for Thermodynamic Simulations with High-Dimensional Input Spaces Using Sparse Polynomial Chaos Expansion: Retrofit of a Large Thermal Power Plant

Applied Sciences, 2023
The assessment of the future thermodynamics performance of a retrofitted heat and power production unit is prone to many uncertainties due to the large number of parameters involved in the modeling of all its components.
Roeland De Meulenaere, Diederik Coppitters, Ale Sikkema, Tim Maertens, Julien Blondeau   +4 more
doaj   +1 more source

A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses

IEEE Access, 2020
This paper presents an innovative modeling strategy for the construction of efficient and compact surrogate models for the uncertainty quantification of time-domain responses of digital links. The proposed approach relies on a two-step methodology. First,
Paolo Manfredi, Riccardo Trinchero
doaj   +1 more source

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

Frontiers in Materials, 2022
Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models.
Anh Tran , Tim Wildey , Hojun Lim 
doaj   +1 more source

Sparse polynomial chaos expansion for universal stochastic kriging

Journal of Computational and Applied Mathematics
Surrogate modelling techniques have opened up new possibilities to overcome the limitations of computationally intensive numerical models in various areas of engineering and science. However, while fundamental in many engineering applications and decision-making, the incorporation of uncertainty quantification into meta-models remains a challenging ...
José Carlos García-Merino, Carmen Calvo-Jurado, Enrique García-Macías   +2 more
openaire   +4 more sources

Adaptive sparse polynomial chaos expansions: A survey

, 2019
ISBN:978-3-903024-84 ...
Lüthen, Nora; id_orcid0000-0002-3765-4222, Sudret, Bruno; id_orcid0000-0002-9501-7395   +1 more
openaire   +3 more sources

Adaptive Sparse Polynomial Chaos Expansions via Leja Interpolation

CoRR, 2019
22 pages, 4 ...
Dimitrios Loukrezis, Herbert De Gersem
openaire   +2 more sources

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions [PDF]

Journal of Computational Physics, 2017
In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect.
Roland Schöbi, Bruno Sudret
openaire   +3 more sources

RELIABILITY ANALYSIS ON HYBRID SURROGATE MODEL OF RADIAL BASIS FUNCTION AND SPARSE POLYNOMIAL CHAOS EXPANSION (MT)

Jixie qiangdu, 2023
To resolve the poor universality and low accuracy of the existing surrogate models for reliability analysis, a hybrid surrogate model based on radial basis function（RBF） and sparse polynomial chaotic expansion（SPCE） was proposed.
ZHAO ZiDa, ZHANG DeQuan, OUYANG Heng, WU ZePing   +3 more
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