Results 31 to 40 of about 13,481 (226)
STOCHASTIC POLYNOMIAL CHAOS EXPANSIONS TO EMULATE STOCHASTIC SIMULATORS
International Journal for Uncertainty Quantification, 2023 In the context of uncertainty quantification, computational models are required to be repeatedly evaluated. This task is intractable for costly numerical models. Such a problem turns out to be even more severe for stochastic simulators, the output of which is a random variable for a given set of input parameters.
Zhu, Xujia +1 more
openaire +3 more sources
MECHANICAL STRUCTURAL RELIABILITY ANALYSIS BASED ON POLYNOMIAL CHAOS EXPANSIONS
Jixie qiangdu, 2022 Reliability is one of important index in the analysis and evaluation of mechanical structure. Aiming at the problems of various failure modes and low efficiency of reliability evaluation for complex mechanical structures, the reliability analysis method ...
WANG ZhiMing +4 more
doaj
Uncertainty quantification in the design of wireless power transfer systems
Open Physics, 2020 The paper addresses the uncertainty quantification of physical and geometrical material parameters in the design of wireless power transfer systems. For 3D complex systems, a standard Monte Carlo cannot be directly used to extract statistical quantities.
Pei Yao +3 more
doaj +1 more source
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
core +2 more sources
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
openaire +4 more sources
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
doaj +1 more source
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
core +1 more source
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
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
doaj +1 more source
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
doaj +1 more source