Academic literature on the topic 'SSFEM'

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Journal articles on the topic "SSFEM"

1

Gaignaire, R., F. Guyomarc'h, O. Moreau, S. Clenet, and B. Sudret. "Speeding Up SSFEM Computation Using Kronecker Tensor Products." IEEE Transactions on Magnetics 45, no. 3 (2009): 1432–35. http://dx.doi.org/10.1109/tmag.2009.2012662.

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2

Poński, Mariusz. "Time integration of stochastic generalized equations of motion using SSFEM." Journal of Theoretical and Applied Mechanics 57, no. 1 (2019): 37–48. http://dx.doi.org/10.15632/jtam-pl.57.1.37.

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3

Farah, Khaled, Mounir Ltifi, and Hedi Hassis. "A Study of Probabilistic FEMs for a Slope Reliability Analysis Using the Stress Fields." Open Civil Engineering Journal 9, no. 1 (2015): 196–206. http://dx.doi.org/10.2174/1874149501509010196.

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In this paper, the applicability and the effectiveness of the probabilistic finite element methods (FEMs) such as the perturbation method, and the Spectral Stochastic Finite Element Method (SSFEM) applied to the reliability analysis of the slope stability have been studied. The results were checked by the Monte Carlo simulation and a direct coupling ap-proach combining the deterministic finite elements code and First Order Reliability Method (FORM) algorithm. These methods are presented considering the spatial variation of soil strength parameters and Young modulus. The random field is used to
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4

KAMITAIRA, Kenta, Kazuhiro KORO, and Kazuhisa ABE. "SSFEM-based cyclic deformation analysis considering spatial variation of elastoplastic behavior of ballast material." Proceedings of The Computational Mechanics Conference 2019.32 (2019): 172. http://dx.doi.org/10.1299/jsmecmd.2019.32.172.

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5

Hussein, A., M. El-Tawil, W. El-Tahan, and A. A. Mahmoud. "Solution of randomly excited stochastic differential equations with stochastic operator using spectral stochastic finite element method (SSFEM)." Structural Engineering and Mechanics 28, no. 2 (2008): 129–52. http://dx.doi.org/10.12989/sem.2008.28.2.129.

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6

Pranesh, Srikara, and Debraj Ghosh. "Addressing the curse of dimensionality in SSFEM using the dependence of eigenvalues in KL expansion on domain size." Computer Methods in Applied Mechanics and Engineering 311 (November 2016): 457–75. http://dx.doi.org/10.1016/j.cma.2016.08.023.

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7

Collectif. "SSFAM." Bulletin 1024, no. 14 (November 2019): 22–24. http://dx.doi.org/10.48556/sif.1024.14.22.

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8

Lee, Philip. "SSIEM column." Journal of Inherited Metabolic Disease 29, no. 1 (2006): 2. http://dx.doi.org/10.1007/s10545-006-0002-z.

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9

Blau, Nenad, and Brian Fowler. "SSIEM 2011." Journal of Inherited Metabolic Disease 35, no. 4 (2012): 569. http://dx.doi.org/10.1007/s10545-012-9501-2.

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10

Huang, Xi, Tiecheng Bai, Huade Guan, Xiayong Wei, Yali Wang, and Xiaomin Mao. "An Improved Exponential Model Considering a Spectrally Effective Moisture Threshold for Proximal Hyperspectral Reflectance Simulation and Soil Salinity Estimation." Remote Sensing 14, no. 24 (2022): 6396. http://dx.doi.org/10.3390/rs14246396.

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Soil salinization has become one of the main factors restricting sustainable development of agriculture. Field spectrometry provides a quick way to predict the soil salinization. However, soil moisture content (SMC) seriously interferes with the spectral information of saline soil in arid areas. It is vital to establish a model that is insensitive to SMC for potential in situ field applications. The soil spectral reflectance exponential model (SSREM) has been widely employed for reflectance simulation and SSC inversion. However, its reliability for saline soils with high SMC has not been verif
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