Results 31 to 40 of about 12,137 (169)
Polynomial chaos expansions for damped oscillators
, 2015 Uncertainty quantification is the state-of-the-art framework dealing with uncertainties arising in all kind of real-life problems. One of the framework’s functions is to propagate uncertainties from the stochastic input factors to the output quantities of interest, hence the name uncertainty propagation.
Mai, Chu V., Sudret, Bruno
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Uncertainty Quantification for Airfoil Icing using Polynomial Chaos Expansions [PDF]
, 2014 The formation and accretion of ice on the leading edge of a wing can be detrimental to airplane performance. Complicating this reality is the fact that even a small amount of uncertainty in the shape of the accreted ice may result in a large amount of ...
DeGennaro, Anthony M. +2 more
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Computation, 2022 In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great ...
Georgios Karagiannis +3 more
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Asymptotic expansion for some local volatility models arising in finance [PDF]
, 2018 In this paper we study the small noise asymptotic expansions for certain classes of local volatility models arising in finance. We provide explicit expressions for the involved coefficients as well as accurate estimates on the remainders.
ALbeverio, Sergio +3 more
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Fast Shielding Optimization of an Inductive Power Transfer System for Electric Vehicles
IEEE Access, 2022 The shielding design is one of the most difficult phases in developing an inductive power transfer system (IPT) for electric vehicles. In this aspect, the combination of metamodeling with a multiobjective optimization algorithm provides an efficient ...
Yao Pei +4 more
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Data-driven sparse polynomial chaos expansion for models with dependent inputs
Journal of Safety Science and Resilience, 2023 Polynomial chaos expansions (PCEs) have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of the inputs.
Zhanlin Liu, Youngjun Choe
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Uncertainty quantification for fat-tailed probability distributions in aircraft engine simulations [PDF]
, 2017 Rare event simulation is vital for industrial design because some events, so-called black swans, can have fatal consequences despite their low probability of occurrence.
Ahlfeld, R +3 more
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Technologies, 2019 The uncertainties in various Electromagnetic (EM) problems may present a significant effect on the properties of the involved field components, and thus, they must be taken into consideration.
Christos Salis +2 more
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Sparse Polynomial Chaos expansions using variational relevance vector machines [PDF]
Journal of Computational Physics, 2020 The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations. These challenges can be addressed by enforcing sparsity in the series representation through retaining only the most important basis terms.
Panagiotis Tsilifis +3 more
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Generalized decoupled polynomial chaos for nonlinear circuits with many random parameters [PDF]
, 2015 This letter proposes a general and effective decoupled technique for the stochastic simulation of nonlinear circuits via polynomial chaos. According to the standard framework, stochastic circuit waveforms are still expressed as expansions of orthonormal ...
Canavero, Flavio +3 more
core +2 more sources