Results 71 to 80 of about 7,268 (236)
Time‐Dependent Polynomial Chaos
AIP Conference Proceedings, 2008 Conventional generalized polynomial chaos is known to fail for long time integration, loosing its optimal convergence behaviour and developing unacceptable error levels. The reason for this loss of convergence is the assumption that the probability density function is constant in time.
Marc Gerritsma +2 more
openaire +4 more sources
An Efficient Polynomial Chaos Method for Stiffness Analysis of Air Spring Considering Uncertainties
Complexity, 2021 Traditional methods for stiffness analysis of the air spring are based on deterministic assumption that the parameters are fixed. However, uncertainties have widely existed, and the mechanic property of the air spring is very sensitive to these ...
Feng Kong, Penghao Si, Shengwen Yin
doaj +1 more source
Solving Stochastic Climate‐Economy Models: A Deep Least‐Squares Monte Carlo Approach
Mathematical Finance, EarlyView.ABSTRACT Stochastic versions of recursive integrated climate‐economy assessment models are essential for studying and quantifying policy decisions under uncertainty. However, as the number of state variables and stochastic shocks increases, solving these models via deterministic grid‐based dynamic programming (e.g., value‐function iteration/projection ...
Aleksandar Arandjelović +4 more
wiley +1 more source
Swiss Political Science Review, EarlyView.Abstract This study analyses the success of populist radical right (PRR) parties in the 2023 Swiss elections using reference group theory. While existing literature emphasizes the influence of objective and subjective group membership on electoral choice, it often overlooks voters' feelings toward groups they do not belong to and their perceptions of ...
Anke Tresch, Line Rennwald
wiley +1 more source
Shock and Vibration, 2016 For the frequency response analysis of acoustic field with random and interval parameters, a nonintrusive uncertain analysis method named Polynomial Chaos Response Surface (PCRS) method is proposed.
Mingjie Wang, Zhimin Wan, Qibai Huang
doaj +1 more source
A Class of Quadratic Polynomial Chaotic Maps and Their Fixed Points Analysis
Entropy, 2019 When chaotic systems are used in different practical applications, such as chaotic secure communication and chaotic pseudorandom sequence generators, a large number of chaotic systems are strongly required.
Chuanfu Wang, Qun Ding
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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 +2 more sources
The Journal of Physiology, EarlyView.Abstract figure legend Graphical representation of methods. We implemented three biventricular geometric models (Zenger et al., 2020) with rule‐based myocardial fibre orientations (Bayer et al., 2018). We evaluated variability in the fibre orientation via four sets of parameter distributions to determine the role of the primary and imbrication angles ...
Lindsay C. R. Tanner +8 more
wiley +1 more source
Polynomials and General Degree-Based Topological Indices of Generalized Sierpinski Networks
Complexity, 2021 Sierpinski networks are networks of fractal nature having several applications in computer science, music, chemistry, and mathematics. These networks are commonly used in chaos, fractals, recursive sequences, and complex systems.
Chengmei Fan +4 more
doaj +1 more source
Stochastic Finite Element Method Using Polynomial Chaos Expansion
, 2011 We present a new response surface based stochastic finite element method to obtain solutions for general random uncertainty problems using the polynomial chaos expansion.
Wei Zhao, Ji Ke Liu
core +1 more source