Results 11 to 20 of about 691 (194)
On solving stochastic collocation systems with algebraic multigrid [PDF]
IMA Journal of Numerical Analysis, 2010 Stochastic collocation methods facilitate the numerical solution of partial differential equations (PDEs) with random data and give rise to long sequences of similar linear systems. When elliptic PDEs with random diffusion coefficients are discretized with mixed finite element methods in the physical domain we obtain saddle point systems.
Gordon, Andrew, Powell, Catherine
openaire +4 more sources
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
Stochastic collocation with kernel density estimation [PDF]
Computer Methods in Applied Mechanics and Engineering, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elman, Howard C., Miller, Christopher W.
openaire +2 more sources
Assessing the Structural Performance of Biodegradable Capsules
Applied Sciences, 2023 Biodegradable materials pose challenges over all aspects of computational mechanics. In this study, the focus is on the resulting domain uncertainty. Model structures or devices are shells of revolution subject to random variation of the outer surface ...
Harri Hakula
doaj +1 more source
An Adaptive WENO Collocation Method for Differential Equations with Random Coefficients
Mathematics, 2016 The stochastic collocation method for solving differential equations with random inputs has gained lots of popularity in many applications, since such a scheme exhibits exponential convergence with smooth solutions in the random space.
Wei Guo +3 more
doaj +1 more source
A multilevel stochastic collocation method for SPDEs [PDF]
AIP Conference Proceedings, 2015 We present a multilevel stochastic collocation method that, as do multilevel Monte Carlo methods, uses a hierarchy of spatial approximations to reduce the overall computational complexity when solving partial differential equations with random inputs.
Max Gunzburger +3 more
openaire +3 more sources
Stochastic Collocation for Optimal Control Problems with Stochastic PDE Constraints [PDF]
SIAM Journal on Control and Optimization, 2012 We discuss the use of stochastic collocation for the solution of optimal control problems which are constrained by stochastic partial differential equations (SPDE). Thereby the constraining SPDE depends on data which is not deterministic but random. Assuming a deter- ministic control, randomness within the states of the input data will propagate to the
Hanne Tiesler +3 more
openaire +1 more source
Shock and Vibration, 2018 Numerical simulation of thin solids remains one of the challenges in computational mechanics. The 3D elasticity problems of shells of revolution are dimensionally reduced in different ways depending on the symmetries of the configurations resulting in ...
Harri Hakula +2 more
doaj +1 more source
IEEE Access, 2020 Semiconductor fabrication technologies as applies to the nanometer-era paradigms of nowadays have rendered uncertainty quantification analyses through component-level parameters compulsory and indispensable. Frequency responses of CMOS active filters are
Mecit Emre Duman, Onder Suvak
doaj +1 more source
Известия Иркутского государственного университета: Серия "Математика", 2022 The polynomial spline collocation method is proposed for solution of Volterra integral equations of the first kind with special piecewise continuous kernels.
Aleksandr Tynda +2 more
doaj +1 more source