Log in

Relevant bibliographies by topics / Polynomial chaos expansion / Journal articles

To see the other types of publications on this topic, follow the link: Polynomial chaos expansion.

Journal articles on the topic 'Polynomial chaos expansion'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Polynomial chaos expansion.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

SEPAHVAND, K., S. MARBURG, and H. J. HARDTKE. "UNCERTAINTY QUANTIFICATION IN STOCHASTIC SYSTEMS USING POLYNOMIAL CHAOS EXPANSION." International Journal of Applied Mechanics 02, no. 02 (2010): 305–53. http://dx.doi.org/10.1142/s1758825110000524.

Full text

Abstract:

In recent years, extensive research has been reported about a method which is called the generalized polynomial chaos expansion. In contrast to the sampling methods, e.g., Monte Carlo simulations, polynomial chaos expansion is a nonsampling method which represents the uncertain quantities as an expansion including the decomposition of deterministic coefficients and random orthogonal bases. The generalized polynomial chaos expansion uses more orthogonal polynomials as the expansion bases in various random spaces which are not necessarily Gaussian. A general review of uncertainty quantification

APA, Harvard, Vancouver, ISO, and other styles

2

Yin, Shengwen, Xiaohan Zhu, and Xiang Liu. "A Novel Sparse Polynomial Expansion Method for Interval and Random Response Analysis of Uncertain Vibro-Acoustic System." Shock and Vibration 2021 (September 23, 2021): 1–15. http://dx.doi.org/10.1155/2021/1125373.

Full text

Abstract:

For the vibro-acoustic system with interval and random uncertainties, polynomial chaos expansions have received broad and persistent attention. Nevertheless, the cost of the computation process increases sharply with the increasing number of uncertain parameters. This study presents a novel interval and random polynomial expansion method, called Sparse Grids’ Sequential Sampling-based Interval and Random Arbitrary Polynomial Chaos (SGS-IRAPC) method, to obtain the response of a vibro-acoustic system with interval and random uncertainties. The proposed SGS-IRAPC retains the accuracy and the sim

APA, Harvard, Vancouver, ISO, and other styles

3

SEPAHVAND, K., S. MARBURG, and H. J. HARDTKE. "STOCHASTIC STRUCTURAL MODAL ANALYSIS INVOLVING UNCERTAIN PARAMETERS USING GENERALIZED POLYNOMIAL CHAOS EXPANSION." International Journal of Applied Mechanics 03, no. 03 (2011): 587–606. http://dx.doi.org/10.1142/s1758825111001147.

Full text

Abstract:

In this paper, the application of generalized polynomial chaos expansion in stochastic structural modal analysis including uncertain parameters is investigated. We review the theory of polynomial chaos and relating error analysis. A general formulation for the representation of modal problems by the polynomial chaos expansion is derived. It shows how the modal frequencies and modal shapes are influenced by the parameter uncertainties. The key issues that arise in the polynomial chaos simulation of modal analysis are discussed for two examples: a discrete 2-DOF system and continuous model of a

APA, Harvard, Vancouver, ISO, and other styles

4

Ghanem, R. "The Nonlinear Gaussian Spectrum of Log-Normal Stochastic Processes and Variables." Journal of Applied Mechanics 66, no. 4 (1999): 964–73. http://dx.doi.org/10.1115/1.2791806.

Full text

Abstract:

A procedure is presented in this paper for developing a representation of lognormal stochastic processes via the polynomial chaos expansion. These are processes obtained by applying the exponential operator to a gaussian process. The polynomial chaos expansion results in a representation of a stochastic process in terms of multidimensional polynomials orthogonal with respect to the gaussian measure with the dimension defined through a set of independent normalized gaussian random variables. Such a representation is useful in the context of the spectral stochastic finite element method, as well

APA, Harvard, Vancouver, ISO, and other styles

5

Zhao, Wei, and Ji Ke Liu. "Stochastic Finite Element Method Using Polynomial Chaos Expansion." Advanced Materials Research 199-200 (February 2011): 500–504. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.500.

Full text

Abstract:

We present a new response surface based stochastic finite element method to obtain solutions for general random uncertainty problems using the polynomial chaos expansion. The approach is general but here a typical elastostatics example only with the random field of Young's modulus is presented to illustrate the stress analysis, and computational comparison with the traditional polynomial expansion approach is also performed. It shows that the results of the polynomial chaos expansion are improved compared with that of the second polynomial expansion method.

APA, Harvard, Vancouver, ISO, and other styles

6

Yin, Shengwen, Yuan Gao, Xiaohan Zhu, and Zhonggang Wang. "Anisotropy-Based Adaptive Polynomial Chaos Method for Hybrid Uncertainty Quantification and Reliability-Based Design Optimization of Structural-Acoustic System." Mathematics 11, no. 4 (2023): 836. http://dx.doi.org/10.3390/math11040836.

Full text

Abstract:

The evaluation of objective functions and component reliability in the optimisation of structural-acoustic systems with random and interval variables is computationally expensive, especially when strong nonlinearity exhibits between the response and input variables. To reduce the computational cost and improve the computational efficiency, a novel anisotropy-based adaptive polynomial chaos (ABAPC) expansion method was developed in this study. In ABAPC, the anisotropy-based polynomial chaos expansion, namely the retained order of polynomial chaos expansion (PCE) differs from each variable, is u

APA, Harvard, Vancouver, ISO, and other styles

7

Abbasi, Mostafa, and Ali Gholami. "Polynomial chaos expansion for nonlinear geophysical inverse problems." GEOPHYSICS 82, no. 4 (2017): R259—R268. http://dx.doi.org/10.1190/geo2016-0716.1.

Full text

Abstract:

There are lots of geophysical problems that include computationally expensive functions (forward models). Polynomial chaos (PC) expansion aims to approximate such an expensive equation or system with a polynomial expansion on the basis of orthogonal polynomials. Evaluation of this expansion is extremely fast because it is a polynomial function. This property of the PC expansion is of great importance for stochastic problems, in which an expensive function needs to be evaluated thousands of times. We have developed PC expansion as a novel technique to solve nonlinear geophysical problems. To be

APA, Harvard, Vancouver, ISO, and other styles

8

Gao, Rugao, Keping Zhou, and Yun Lin. "A Flexible Polynomial Expansion Method for Response Analysis with Random Parameters." Complexity 2018 (December 3, 2018): 1–14. http://dx.doi.org/10.1155/2018/7471460.

Full text

Abstract:

The generalized Polynomial Chaos Expansion Method (gPCEM), which is a random uncertainty analysis method by employing the orthogonal polynomial bases from the Askey scheme to represent the random space, has been widely used in engineering applications due to its good performance in both computational efficiency and accuracy. But in gPCEM, a nonlinear transformation of random variables should always be used to adapt the generalized Polynomial Chaos theory for the analysis of random problems with complicated probability distributions, which may introduce nonlinearity in the procedure of random u

APA, Harvard, Vancouver, ISO, and other styles

9

Crestaux, Thierry, Olivier Le Maıˆtre, and Jean-Marc Martinez. "Polynomial chaos expansion for sensitivity analysis." Reliability Engineering & System Safety 94, no. 7 (2009): 1161–72. http://dx.doi.org/10.1016/j.ress.2008.10.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

LePage, Kevin D. "Polynomial chaos expansions for modelling the statistics of acoustic propagation in random waveguides." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A279. http://dx.doi.org/10.1121/10.0027498.

Full text

Abstract:

The use of Polynomial Chaos expansions to model the transfer of the statistical properties of the sound speed in ocean waveguides to those of acoustic waves passing through them is described. A perturbational framework approximating the interaction of acoustic normal modes with fluctuations around a background sound speed is used, with the fluctuations being represented vertically by empirical orthogonal functions and horizontally by correlation functions. The Polynomial Chaos expansions are derived to second order in the uncorrelated random variable representing the sound speed fluctuations f

APA, Harvard, Vancouver, ISO, and other styles

11

Gayrard, Emeline, Cédric Chauvière, Hacène Djellout, and Pierre Bonnet. "MODELING EXPERIMENTAL DATA WITH POLYNOMIALS CHAOS." Probability in the Engineering and Informational Sciences 34, no. 1 (2018): 14–26. http://dx.doi.org/10.1017/s026996481800030x.

Full text

Abstract:

Given a raw data sample, the purpose of this paper is to design a numerical procedure to model this sample under the form of polynomial chaos expansion. The coefficients of the polynomial are computed as the solution to a constrained optimization problem. The procedure is first validated on samples coming from a known distribution and it is then applied to raw experimental data of unknown distribution. Numerical experiments show that only five coefficients of the Chaos expansions are required to get an accurate representation of a sample.

APA, Harvard, Vancouver, ISO, and other styles

12

Vu, M. T., A. Jardani, A. Revil, and M. Jessop. "Magnetometric resistivity tomography using chaos polynomial expansion." Geophysical Journal International 221, no. 3 (2020): 1469–83. http://dx.doi.org/10.1093/gji/ggaa082.

Full text

Abstract:

SUMMARY We present an inversion algorithm to reconstruct the spatial distribution of the electrical conductivity from the analysis of magnetometric resistivity (MMR) data acquired at the ground surface. We first review the theoretical background of MMR connecting the generation of a magnetic field in response to the injection of a low-frequency current source and sink in the ground given a known distribution of electrical conductivity in the subsurface of the Earth. The forward modelling is based on sequentially solving the Poisson equation for the electrical potential distribution and the mag

APA, Harvard, Vancouver, ISO, and other styles

13

Paffrath, M., and U. Wever. "Adapted polynomial chaos expansion for failure detection." Journal of Computational Physics 226, no. 1 (2007): 263–81. http://dx.doi.org/10.1016/j.jcp.2007.04.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Di Persio, Luca, Gregorio Pellegrini, and Michele Bonollo. "Polynomial Chaos Expansion Approach to Interest Rate Models." Journal of Probability and Statistics 2015 (2015): 1–24. http://dx.doi.org/10.1155/2015/369053.

Full text

Abstract:

The Polynomial Chaos Expansion (PCE) technique allows us to recover a finite second-order random variable exploiting suitable linear combinations of orthogonal polynomials which are functions of a given stochastic quantityξ, hence acting as a kind of random basis. The PCE methodology has been developed as a mathematically rigorous Uncertainty Quantification (UQ) method which aims at providing reliable numerical estimates for some uncertain physical quantities defining the dynamic of certain engineering models and their related simulations. In the present paper, we use the PCE approach in order

APA, Harvard, Vancouver, ISO, and other styles

15

SEPAHVAND, K., S. MARBURG, and H. J. HARDTKE. "NUMERICAL SOLUTION OF ONE-DIMENSIONAL WAVE EQUATION WITH STOCHASTIC PARAMETERS USING GENERALIZED POLYNOMIAL CHAOS EXPANSION." Journal of Computational Acoustics 15, no. 04 (2007): 579–93. http://dx.doi.org/10.1142/s0218396x07003524.

Full text

Abstract:

This paper presents a numerical algorithm which is using generalized polynomial chaos combined with the finite difference method for the solution of the one-dimensional wave equation with stochastic physical parameters. The stochastic parameters are represented by the Hermite polynomial chaos. A spectral–finite difference model for the numerical solution is introduced using generalized polynomial chaos expansion. The general conditions for convergence and stability of numerical algorithms are derived. Finally, the method is applied to a vibrating string. Results are compared with those of a Mo

APA, Harvard, Vancouver, ISO, and other styles

16

Petzke, Felix, Ali Mesbah, and Stefan Streif. "PoCET: a Polynomial Chaos Expansion Toolbox for Matlab." IFAC-PapersOnLine 53, no. 2 (2020): 7256–61. http://dx.doi.org/10.1016/j.ifacol.2020.12.560.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Gomes, Wellison José de S., André Teófilo Beck, and Cláudio R. A. da Silva Jr. "Modeling random corrosion processes via polynomial chaos expansion." Journal of the Brazilian Society of Mechanical Sciences and Engineering 34, spe2 (2012): 561–68. http://dx.doi.org/10.1590/s1678-58782012000600004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Lefebvre, Tom. "On Moment Estimation From Polynomial Chaos Expansion Models." IEEE Control Systems Letters 5, no. 5 (2021): 1519–24. http://dx.doi.org/10.1109/lcsys.2020.3040851.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Soize, C. "Polynomial Chaos Expansion of a Multimodal Random Vector." SIAM/ASA Journal on Uncertainty Quantification 3, no. 1 (2015): 34–60. http://dx.doi.org/10.1137/140968495.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Rawal, Keerti, and Aijaz Ahmad. "Stochastic Economic Dispatch with Advanced Polynomial Chaos Expansion." IFAC-PapersOnLine 57 (2024): 155–60. http://dx.doi.org/10.1016/j.ifacol.2024.05.027.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Jacquelin, E., M. I. Friswell, S. Adhikari, O. Dessombz, and J. J. Sinou. "Polynomial chaos expansion with random and fuzzy variables." Mechanical Systems and Signal Processing 75 (June 2016): 41–56. http://dx.doi.org/10.1016/j.ymssp.2015.12.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Torchio, R., L. Di Rienzo, and L. Codecasa. "Stochastic PEEC Method Based on Polynomial Chaos Expansion." IEEE Transactions on Magnetics 55, no. 6 (2019): 1–4. http://dx.doi.org/10.1109/tmag.2019.2908588.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Abbasi, Mostafa, and Ali Gholami. "Automatic nonhyperbolic velocity analysis by polynomial chaos expansion." GEOPHYSICS 83, no. 6 (2018): U79—U88. http://dx.doi.org/10.1190/geo2017-0478.1.

Full text

Abstract:

Seismic velocity analysis is one of the most crucial and, at the same time, the most laborious tasks during seismic data processing. This becomes even more difficult and time-consuming when nonhyperbolicity has to be considered in the velocity analysis. Nonhyperbolic velocity analysis provides very useful information during the processing and interpretation of seismic data. The most common approach for considering anisotropy during velocity analysis is to describe the moveout based on a nonhyperbolic equation. The nonhyperbolic moveout equation in vertically transverse isotropic (VTI) media is

APA, Harvard, Vancouver, ISO, and other styles

24

Massoud, Elias C. "Emulation of environmental models using polynomial chaos expansion." Environmental Modelling & Software 111 (January 2019): 421–31. http://dx.doi.org/10.1016/j.envsoft.2018.10.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Rahman, Sharif. "A polynomial chaos expansion in dependent random variables." Journal of Mathematical Analysis and Applications 464, no. 1 (2018): 749–75. http://dx.doi.org/10.1016/j.jmaa.2018.04.032.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Ghanem, Roger, and P. D. Spanos. "Polynomial Chaos in Stochastic Finite Elements." Journal of Applied Mechanics 57, no. 1 (1990): 197–202. http://dx.doi.org/10.1115/1.2888303.

Full text

Abstract:

A new method for the solution of problems involving material variability is proposed. The material property is modeled as a stochastic process. The method makes use of a convergent orthogonal expansion of the process. The solution process is viewed as an element in the Hilbert space of random functions, in which a sequence of projection operators is identified as the polynomial chaos of consecutive orders. Thus, the solution process is represented by its projections onto the spaces spanned by these polynomials. The proposed method involves a mathematical formulation which is a natural extensio

APA, Harvard, Vancouver, ISO, and other styles

27

Xiu, Dongbin, Didier Lucor, C. H. Su, and George Em Karniadakis. "Stochastic Modeling of Flow-Structure Interactions Using Generalized Polynomial Chaos." Journal of Fluids Engineering 124, no. 1 (2001): 51–59. http://dx.doi.org/10.1115/1.1436089.

Full text

Abstract:

We present a generalized polynomial chaos algorithm to model the input uncertainty and its propagation in flow-structure interactions. The stochastic input is represented spectrally by employing orthogonal polynomial functionals from the Askey scheme as the trial basis in the random space. A standard Galerkin projection is applied in the random dimension to obtain the equations in the weak form. The resulting system of deterministic equations is then solved with standard methods to obtain the solution for each random mode. This approach is a generalization of the original polynomial chaos expa

APA, Harvard, Vancouver, ISO, and other styles

28

Duan, Hui, and Giray Okten. "Control variate Monte Carlo estimators based on sparse polynomial chaos expansions." Socio-Environmental Systems Modelling 5 (December 4, 2023): 18568. http://dx.doi.org/10.18174/sesmo.18568.

Full text

Abstract:

We introduce two control variate Monte Carlo estimators where the control is based on the truncated sparse polynomial chaos expansion of the function in hand. We use the control variate estimators to estimate the lower and upper Sobol' indices in some applications, and compare them numerically with some of the best Monte Carlo estimators in the literature. The results suggest that in computationally expensive problems where a low-order polynomial chaos expansion is not an accurate approximation of the model but highly correlated with it, the control variate estimators are either the best or am

APA, Harvard, Vancouver, ISO, and other styles

29

Sraj, Ihab, Mohamed Iskandarani, W. Carlisle Thacker, Ashwanth Srinivasan, and Omar M. Knio. "Drag Parameter Estimation Using Gradients and Hessian from a Polynomial Chaos Model Surrogate." Monthly Weather Review 142, no. 2 (2014): 933–41. http://dx.doi.org/10.1175/mwr-d-13-00087.1.

Full text

Abstract:

Abstract A variational inverse problem is solved using polynomial chaos expansions to infer several critical variables in the Hybrid Coordinate Ocean Model’s (HYCOM’s) wind drag parameterization. This alternative to the Bayesian inference approach in Sraj et al. avoids the complications of constructing the full posterior with Markov chain Monte Carlo sampling. It focuses instead on identifying the center and spread of the posterior distribution. The present approach leverages the polynomial chaos series to estimate, at very little extra cost, the gradients and Hessian of the cost function duri

APA, Harvard, Vancouver, ISO, and other styles

30

Slika, Wael, and George Saad. "A practical polynomial chaos Kalman filter implementation using nonlinear error projection on a reduced polynomial chaos expansion." International Journal for Numerical Methods in Engineering 112, no. 12 (2017): 1869–85. http://dx.doi.org/10.1002/nme.5586.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Zhou, Yicheng, and Zhenzhou Lu. "Active Polynomial Chaos Expansion for Reliability-Based Design Optimization." AIAA Journal 57, no. 12 (2019): 5431–46. http://dx.doi.org/10.2514/1.j058020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Newberry, Felix, Jerrad Hampton, Kenneth Jansen, and Alireza Doostan. "Bi-fidelity reduced polynomial chaos expansion for uncertainty quantification." Computational Mechanics 69, no. 2 (2021): 405–24. http://dx.doi.org/10.1007/s00466-021-02096-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Novák, Lukáš, Miroslav Vořechovský, Václav Sadílek, and Michael D. Shields. "Variance-based adaptive sequential sampling for Polynomial Chaos Expansion." Computer Methods in Applied Mechanics and Engineering 386 (December 2021): 114105. http://dx.doi.org/10.1016/j.cma.2021.114105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Spina, Domenico, Francesco Ferranti, Tom Dhaene, Luc Knockaert, Giulio Antonini, and Dries Vande Ginste. "Variability Analysis of Multiport Systems Via Polynomial-Chaos Expansion." IEEE Transactions on Microwave Theory and Techniques 60, no. 8 (2012): 2329–38. http://dx.doi.org/10.1109/tmtt.2012.2202685.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Palar, Pramudita Satria, Lavi Rizki Zuhal, Koji Shimoyama, and Takeshi Tsuchiya. "Global sensitivity analysis via multi-fidelity polynomial chaos expansion." Reliability Engineering & System Safety 170 (February 2018): 175–90. http://dx.doi.org/10.1016/j.ress.2017.10.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Zhou, Yicheng, Zhenzhou Lu, and Wanying Yun. "Active sparse polynomial chaos expansion for system reliability analysis." Reliability Engineering & System Safety 202 (October 2020): 107025. http://dx.doi.org/10.1016/j.ress.2020.107025.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Xie, Q., S. Lu, D. Kong, and J. Wang. "Treatment of evacuation time uncertainty using polynomial chaos expansion." Journal of Fire Protection Engineering 23, no. 1 (2013): 31–49. http://dx.doi.org/10.1177/1042391512470578.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Torre, Emiliano, Stefano Marelli, Paul Embrechts, and Bruno Sudret. "Data-driven polynomial chaos expansion for machine learning regression." Journal of Computational Physics 388 (July 2019): 601–23. http://dx.doi.org/10.1016/j.jcp.2019.03.039.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Shao, Qian, Anis Younes, Marwan Fahs, and Thierry A. Mara. "Bayesian sparse polynomial chaos expansion for global sensitivity analysis." Computer Methods in Applied Mechanics and Engineering 318 (May 2017): 474–96. http://dx.doi.org/10.1016/j.cma.2017.01.033.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Panayirci, H. M. "Efficient solution for Galerkin-based polynomial chaos expansion systems." Advances in Engineering Software 41, no. 12 (2010): 1277–86. http://dx.doi.org/10.1016/j.advengsoft.2010.09.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Peng, Ji, Jerrad Hampton та Alireza Doostan. "On polynomial chaos expansion via gradient-enhanced ℓ1-minimization". Journal of Computational Physics 310 (квітень 2016): 440–58. http://dx.doi.org/10.1016/j.jcp.2015.12.049.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Abbaszadeh, Mahmoud, Giannis Moutsinas, Peter J. Thomas, and Weisi Guo. "Uncertainty Quantification in Molecular Signals Using Polynomial Chaos Expansion." IEEE Transactions on Molecular, Biological and Multi-Scale Communications 4, no. 4 (2018): 248–56. http://dx.doi.org/10.1109/tmbmc.2019.2936349.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Cheng, Kai, and Zhenzhou Lu. "Sparse polynomial chaos expansion based on D -MORPH regression." Applied Mathematics and Computation 323 (April 2018): 17–30. http://dx.doi.org/10.1016/j.amc.2017.11.044.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Jacquelin, E., O. Dessombz, J. J. Sinou, S. Adhikari, and M. I. Friswell. "Polynomial chaos-based extended Padé expansion in structural dynamics." International Journal for Numerical Methods in Engineering 111, no. 12 (2017): 1170–91. http://dx.doi.org/10.1002/nme.5497.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Novak, Lukas, and Drahomir Novak. "Polynomial chaos expansion for surrogate modelling: Theory and software." Beton- und Stahlbetonbau 113 (September 2018): 27–32. http://dx.doi.org/10.1002/best.201800048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Novák, Lukáš, Michael D. Shields, Václav Sadílek, and Miroslav Vořechovský. "Active learning-based domain adaptive localized polynomial chaos expansion." Mechanical Systems and Signal Processing 204 (December 2023): 110728. http://dx.doi.org/10.1016/j.ymssp.2023.110728.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Drakos, S., and G. N. Pande. "Stochastic Finite Element Analysis using Polynomial Chaos." Studia Geotechnica et Mechanica 38, no. 1 (2016): 33–43. http://dx.doi.org/10.1515/sgem-2016-0004.

Full text

Abstract:

Abstract This paper presents a procedure of conducting Stochastic Finite Element Analysis using Polynomial Chaos. It eliminates the need for a large number of Monte Carlo simulations thus reducing computational time and making stochastic analysis of practical problems feasible. This is achieved by polynomial chaos expansion of the displacement field. An example of a plane-strain strip load on a semi-infinite elastic foundation is presented and results of settlement are compared to those obtained from Random Finite Element Analysis. A close matching of the two is observed.

APA, Harvard, Vancouver, ISO, and other styles

48

Harati, Ehsan, and Hossein Ahmadi Noubari. "Long Time Prediction of Uncertain Systems Using Singular Perturbation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 05 (2017): 707–21. http://dx.doi.org/10.1142/s0218488517500301.

Full text

Abstract:

This paper considers the problem of long time prediction of uncertain dynamic systems. Spectral methods such as polynomial chaos expansion (PCE) provides a suitable alternative for classical Monte Carlo method with lower computational load. However, polynomial chaos expansion has a major drawback of long time integration error. In this paper, we will apply singular perturbation (SP) method for reducing long time integration error. Using SP the accuracy of long time predictions are improved with comparable computational load. We will apply SP to illustrative exemplify problems to show effective

APA, Harvard, Vancouver, ISO, and other styles

49

Chi, Yaodan, Bin Li, Xiaotian Yang, Tianhao Wang, Kaiyu Yang, and Yinhan Gao. "Research on the Statistical Characteristics of Crosstalk in Naval Ships Wiring Harness Based on Polynomial Chaos Expansion Method." Polish Maritime Research 24, s2 (2017): 205–14. http://dx.doi.org/10.1515/pomr-2017-0084.

Full text

Abstract:

Abstract Crosstalk in wiring harness has been studied extensively for its importance in the naval ships electromagnetic compatibility field. An effective and high-efficiency method is proposed in this paper for analyzing Statistical Characteristics of crosstalk in wiring harness with random variation of position based on Polynomial Chaos Expansion (PCE). A typical 14-cable wiring harness was simulated as the object of research. Distance among interfering cable, affected cable and GND is synthesized and analyzed in both frequency domain and time domain. The model of naval ships wiring harness d

APA, Harvard, Vancouver, ISO, and other styles

50

Sepahvand, Kheirollah, and Steffen Marburg. "Stochastic Dynamic Analysis of Structures with Spatially Uncertain Material Parameters." International Journal of Structural Stability and Dynamics 14, no. 08 (2014): 1440029. http://dx.doi.org/10.1142/s021945541440029x.

Full text

Abstract:

This paper investigates the uncertainty quantification in structural dynamic problems with spatially random variation in material and damping parameters. Uncertain and locally varying material parameters are represented as stochastic field by means of the Karhunen–Loève (KL) expansion. The stiffness and damping properties of the structure are considered uncertain. Stochastic finite element of structural modal analysis is performed in which modal responses are represented using the generalized polynomial chaos (gPC) expansion. Knowing the KL expansions of the random parameters, the nonintrusive

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!