Searched for: subject%3A%22polynomial%255C%2Bchaos%22
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Perko, Z. (author), van der Voort, S.R. (author), Van De Water, Steven (author), Hartman, C.M.H. (author), Hoogeman, M.S. (author), Lathouwers, D. (author)
The highly conformal planned dose distribution achievable in intensity modulated proton therapy (IMPT) can severely be compromised by uncertainties in patient setup and proton range. While several robust optimization approaches have been presented to address this issue, appropriate methods to accurately estimate the robustness of treatment...
journal article 2016
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Schöbi, R. (author), Sudret, B. (author)
In geotechnical applications, mechanical properties of soil vary spatially within the soil mass and they are often represented by random fields. When data at certain locations of the soil mass are available, conditional random fields may be used to incorporate them. In this paper, we combine conditional random fields with sparse polynomial chaos...
conference paper 2015
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Al-Bittar, T. (author), Michael, M. (author), Soubra, A.-H. (author)
In this paper, a probabilistic analysis is presented to compute the ultimate bearing capacity of a strip footing resting on a spatially varying rock mass. The rock is assumed to follow the generalized Hoek-Brown failure criterion. The uniaxial compressive strength of the intact rock (σc) was considered as a random field and the Geological...
conference paper 2015
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Xiao, T. (author), Li, D. (author), Cao, Z. (author), Tang, X. (author)
Stochastic finite element method and random finite element method can provide rigorous tools for slope reliability analysis incorporating spatial variability of soil properties. However, both of them are difficult to be applied into practice due to the modification of finite-element codes and the low efficiency, respectively. To address these...
conference paper 2015
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Perkó, Z. (author)
This thesis presents novel adjoint and spectral methods for the sensitivity and uncertainty (S&U) analysis of multi-physics problems encountered in the field of reactor physics. The first part focuses on the steady state of reactors and extends the adjoint sensitivity analysis methods well established for pure neutron transport problems to...
doctoral thesis 2015
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Loeven, G.J.A. (author)
When modeling physical systems, several sources of uncertainty are present. For example, variability in boundary conditions like free stream velocity or ambient pressure are always present. Furthermore, uncertainties in geometry arise from production tolerances, wear or unknown deformations under loading. Uncertainties in computational fluid...
doctoral thesis 2010
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Loeven, G.J.A. (author), Bijl, H. (author)
In this paper a Two-Step approach is presented for uncertainty quantification for expensive problems with multiple uncertain parameters. Both steps are performed using the Probabilistic Collocation method. The first step consists of a sensitivity analysis to identify the most important parameters of the problem. The sensitivity derivatives are...
journal article 2009
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Witteveen, J.A.S. (author), Bijl, H. (author)
A robust and efficient uncertainty quantification method is presented for resolving the effect of uncertainty on the behavior of multi-physics systems. The extrema diminishing method in probability space maintains a bounded error due to the interpolation of deterministic samples at constant phase in a transonic airfoil flutter problem.
conference paper 2009
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Witteveen, J.A.S. (author)
Physical uncertainties due to atmospheric variations and production tolerances can nowadays have a larger effect on the accuracy of computational predictions than numerical errors. It is essential to quantify the effect of these uncertainties for reducing design safety factors and robust design optimization. This eventually contributes to the...
doctoral thesis 2009
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Witteveen, J.A.S. (author), Bijl, H. (author)
A monomial chaos approach is presented for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equations which can be coupled. The proposed monomial...
journal article 2008
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Gerritsma, M.I. (author), Vos, P. (author), Van der Steen, J.B. (author)
conference paper 2008
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Vos, P. (author)
Generalized polynomial chaos is known to fail for long-term integration, loosing its optimal convergence behavior and developing unacceptable error-levels. In this work, we present a time-dependent alternative of polynomial chaos in order to overcome these issues. This technique exists out of an on-the-fly reinitialization of the polynomial...
master thesis 2006
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Vos, P.E.J. (author), Gerritsma, M.I. (author)
This papers describes the use of the Least-Squares Spectral Element Method to polynomial Chaos to solve stochastic partial differential equations. The method will be described in detail and a comparison will be presented between the least-squares projection and the conventional Galerkin projection.
conference paper 2006
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Vos, P.E.J. (author), Gerritsma, M.I. (author)
This papers describes the use of the Least-Squares Spectral Element Method to polynomial Chaos to solve stochastic partial differential equations. The method will be described in detail and a comparison will be presented between the least-squares projection and the conventional Galerkin projection.
conference paper 2006
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Loeven, A. (author), Witteveen, J.A.S. (author), Bijl, H. (author)
In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in computational fluid-structure interactions is followed. In Step I, a Sensitivity Analysis is used to efficiently narrow the problem down from multiple uncertain parameters to one parameter which has the largest influence on the solution. In Step...
conference paper 2006
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Witteveen, J.A.S. (author), Bijl, H. (author)
Inherent physical uncertainties can have a significant influence on computational predictions. It is therefore important to take physical uncertainties into account to obtain more reliable computational predictions. The Galerkin polynomial chaos method is a commonly applied uncertainty quantification method. However, the polynomial chaos...
conference paper 2006
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Witteveen, J.A.S. (author), Bijl, H. (author)
A monomial chaos approach is proposed for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can still be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equations which can be coupled. The proposed...
conference paper 2006
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Mathelin, L. (author), Le Maitre, O.P. (author)
Accounting for uncertainty in numerical simulations is a growing concern and a great deal of methods have recently been developed, such as the Polynomial Chaos which basically consists in a spectral approximation of the surface response of the solution by stochastic finite elements. However, criteria for refinement of the spectral space have so...
conference paper 2006
document
Loeven, A. (author), Witteveen, J.A.S. (author), Bijl, H. (author)
In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in computational fluid-structure interactions is followed. In Step I, a Sensitivity Analysis is used to efficiently narrow the problem down from multiple uncertain parameters to one parameter which has the largest influence on the solution. In Step...
conference paper 2006
document
Witteveen, J.A.S. (author), Bijl, H. (author)
Inherent physical uncertainties can have a significant influence on computational predictions. It is therefore important to take physical uncertainties into account to obtain more reliable computational predictions. The Galerkin polynomial chaos method is a commonly applied uncertainty quantification method. However, the polynomial chaos...
conference paper 2006
Searched for: subject%3A%22polynomial%255C%2Bchaos%22
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