Ermia Azarkhalili - Academia.edu (original) (raw)
"People who wish to analyze nature without using mathematics must settle for a reduced understanding".
Richard Feynman
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Papers by Ermia Azarkhalili
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The present work develops an innovative method which utilizes sparse recovery to adaptively appro... more The present work develops an innovative method which utilizes sparse recovery to adaptively approximate the solution to stochastic differential equations. The proposed method considers the problem of sparse recovery of unknown coefficients of MWB expansion from a number of solution
realizations smaller than cardinality of unknown coefficients. To illustrate the robustness of developed method, benchmark problems are studied and main statistical moments of solutions obtained by proposed method are compared with the ones derived from Monte Carlo simulations.
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In this paper, a novel method to adaptively approximate the solution to stochastic differential e... more In this paper, a novel method to adaptively approximate the solution to stochastic differential equations, which is based on compressive sampling and sparse recovery, is introduced. The proposed method consider the problem of sparse recovery with respect to multi-wavelet basis (MWB) from a small number of random samples to approximate the solution to problems. To illustrate the robustness of developed method, three benchmark problems are studied and main statistical features of solutions such as the variance and the mean of solutions obtained by proposed method are compared with the ones obtained from Monte Carlo simulations.
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Teaching Documents by Ermia Azarkhalili
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Due date is April 9th 2015
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Bookmarks Related papers MentionsView impact
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Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
The present work develops an innovative method which utilizes sparse recovery to adaptively appro... more The present work develops an innovative method which utilizes sparse recovery to adaptively approximate the solution to stochastic differential equations. The proposed method considers the problem of sparse recovery of unknown coefficients of MWB expansion from a number of solution
realizations smaller than cardinality of unknown coefficients. To illustrate the robustness of developed method, benchmark problems are studied and main statistical moments of solutions obtained by proposed method are compared with the ones derived from Monte Carlo simulations.
Bookmarks Related papers MentionsView impact
In this paper, a novel method to adaptively approximate the solution to stochastic differential e... more In this paper, a novel method to adaptively approximate the solution to stochastic differential equations, which is based on compressive sampling and sparse recovery, is introduced. The proposed method consider the problem of sparse recovery with respect to multi-wavelet basis (MWB) from a small number of random samples to approximate the solution to problems. To illustrate the robustness of developed method, three benchmark problems are studied and main statistical features of solutions such as the variance and the mean of solutions obtained by proposed method are compared with the ones obtained from Monte Carlo simulations.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Due date is April 9th 2015
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact