Modeling and Reducing the Effect of Geometric Uncertainties in Intracranial Aneurysms with Polynomial Chaos Expansion, Data Decomposition, and 4D-Flow MRI.
Sign Up to like & getrecommendations! Published in 2021 at "Cardiovascular engineering and technology"
DOI: 10.1007/s13239-020-00511-w
Abstract: PURPOSE Variations in the vessel radius of segmented surfaces of intracranial aneurysms significantly influence the fluid velocities given by computer simulations. It is important to generate models that capture the effect of these variations in… read more here.
Keywords: fluid velocities; flow mri; uncertainty; intracranial aneurysms ... See more keywords
An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework
Sign Up to like & getrecommendations! Published in 2019 at "Computer Methods in Applied Mechanics and Engineering"
DOI: 10.1016/j.cma.2019.04.046
Abstract: Abstract 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 to construct the experimental design. Therefore, adaptive… read more here.
Keywords: chaos expansion; polynomial chaos; sparse bayesian; adaptive sampling ... See more keywords
Arbitrarily Sparse Polynomial Chaos Expansion for High-Dimensional Parametric Problems: Parametric and Probabilistic Power Flow as an Example
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Systems Journal"
DOI: 10.1109/jsyst.2021.3131479
Abstract: Parameters, no matter whether they are uncontrollable uncertainty factors or controllable variables, have a great impact on the states and performances of continuous engineering systems. Acquiring an explicit expression of the complicated implicit function between… read more here.
Keywords: polynomial chaos; parametric problems; power; chaos expansion ... See more keywords
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
Sign Up to like & getrecommendations! Published in 2023 at "Geoscientific Model Development"
DOI: 10.5194/gmd-16-2037-2023
Abstract: Abstract. The heterogeneous chemistry of atmospheric aerosols involves multiphase chemical kinetics that can be described by kinetic multi-layer models (KMs) that explicitly resolve mass transport and chemical reactions. However, KMs are computationally too expensive to… read more here.
Keywords: polynomial chaos; multiphase chemical; chemistry; machine learning ... See more keywords