Results 11 to 20 of about 720 (191)
Positive Stochastic Collocation for the Collocated Local Volatility Model
, 2021 This paper presents how to apply the stochastic collocation technique to assets that can not move below a boundary. It shows that the polynomial collocation towards a lognormal distribution does not work well. Then, the potentials issues of the related collocated local volatility model (CLV) are explored. Finally, a simple analytical expression for the
Floc'h, Fabien Le +1 more
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Multi-Index Stochastic Collocation for random PDEs [PDF]
Computer Methods in Applied Mechanics and Engineering, 2016 In this work we introduce the Multi-Index Stochastic Collocation method (MISC) for computing statistics of the solution of a PDE with random data. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the space of random data.
AL HajiAli +3 more
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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
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Simplex-stochastic collocation method with improved scalability [PDF]
Journal of Computational Physics, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Edeling, Wouter N. +2 more
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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
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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 D. +1 more
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Risks, 2022 We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined
Shuaiqiang Liu +2 more
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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
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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
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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
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