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10.1098/rsta.2015.0168 http://scihub22266oqcxt.onion/10.1098/rsta.2015.0168 C4810880!4810880!27002065     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Philos+Trans+A+Math+Phys+Eng+Sci 2016 ; 374 (2066): ä Nephropedia Template TP {{tp|p=27002065|t=2016. Some variance reduction methods for numerical stochastic homogenization |pdf=|usr=}}{{27002065}} gab.com Text Some variance reduction methods for numerical stochastic homogenization JO: Philos Trans A Math Phys Eng Sci http://www.kidney.de/pget.php?&tw=1&pmid=27002065 #FOAvip We give an overview of a series of recent studies devoted to variance reduction techniques for numerical stochastic homogenization. Numerical homogenization requires that a set of problems is solved at the microscale, the so-called corrector problems. In a random environment, these problems are stochastic and therefore need to be repeatedly solved, for several configurations of the medium considered. An empirical average over all configurations is then performed using the Monte Carlo approach, so as to approximate the effective coefficients necessary to determine the macroscopic behaviour. Variance severely affects the accuracy and the cost of such computations. Variance reduction approaches, borrowed from other contexts in the engineering sciences, can be useful. Some of these variance reduction techniques are presented, studied and tested here. Twit Text FOAVip Some variance reduction methods for numerical stochastic homogenization ????Philos Trans A Math Phys Eng Sci http://www.kidney.de/pget.php?&tw=1&pmid=27002065 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27002065 #FOAvip English Wikipedia <ref>{{Cite pmid|27002065}}</ref> Some variance reduction methods for numerical stochastic homogenization #MMPMID27002065 Blanc X; Le Bris C; Legoll F Philos Trans A Math Phys Eng Sci 2016[Apr]; 374 (2066): ä PMID27002065show ga We give an overview of a series of recent studies devoted to variance reduction techniques for numerical stochastic homogenization. Numerical homogenization requires that a set of problems is solved at the microscale, the so-called corrector problems. In a random environment, these problems are stochastic and therefore need to be repeatedly solved, for several configurations of the medium considered. An empirical average over all configurations is then performed using the Monte Carlo approach, so as to approximate the effective coefficients necessary to determine the macroscopic behaviour. Variance severely affects the accuracy and the cost of such computations. Variance reduction approaches, borrowed from other contexts in the engineering sciences, can be useful. Some of these variance reduction techniques are presented, studied and tested here. äSome variance reduction methods for numerical stochastic homogenizatio(epmc).pdf DeepDyve Pubget Overpricing