Mohammad Reza Rahimi Tabar
Professor of Physics
Sharif University of Technology
Tel: +98-21-66164551
Email:
mohammed.r.rahimi.tabar(at)uni-oldenburg.de
& rahimitabar(at)sharif.edu
--Book Chapters and Contributions in Encyclopedia:
-- Complexity in the View of Stochastic Processes, R. Friedrich, J. Peinke and M. Reza Rahimi Tabar, Contribution to Encyclopedia of Complexity and System Science, ed. by B. Meyers (Springer Verlag Berlin, 2009) , p. 3574.
-- Short-Term Prediction of Medium and Large-Size Earthquakes Based on Markov and Extended Self-Similarity Analysis of Seismic Data , M. Reza Rahimi Tabar, Muhammad Sahimi, K. Kaviani, M. Allamehzadeh, , J. Peinke, M. Mokhtari, M. Vesaghi, M. D. Niry, F. Ghasemi, A. Bahraminasab, S. Tabatabai and F. Fayazbakhsh., M. Akbari, in: Modelin Critical and Catastrophic Phenomena in Geoscience: A Statistical Physics Approach, Lecture Notes in Physics, 705, pp. 281-301, Springer Verlag, Berlin Heidelberg (2007) (Pdf)
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--Full List of Publications:
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-- Selected Publications:
--Approaching Complexity by Stochastic Methods: From Biological Systems to Turbulence, R. Friedrich, J. Peinke, M. Sahimi, and M. Reza Rahimi Tabar, Physics Reports (2011), in press.
This review addresses a central question in the field of complex systems: Given a fluctuating (in time or space), sequentially measured set of experimental data, how should one analyze the data, assess their underlying trends, and discover the characteristics of the fluctuations that generate the experimental traces? In recent years, significant progress has been made in addressing this question for a class of stochastic processes that can be modeled by Langevin equations, including additive as well as multiplicative fluctuations or noise. Important results have emerged from the analysis of temporal data for such diverse fields as neuroscience, cardiology, finance, economy, surface science, turbulence, seismic time series and epileptic brain dynamics, to name but a few. Furthermore, it has been recognized that a similar approach can be applied to the data that depend on a length scale, such as velocity increments in fully-developed turbulent flow, or height increments that characterize rough surfaces. A basic ingredient of the approach to the analysis of fluctuating data is the presence of a Markovian property, which can be detected in real systems above a certain time or length scale. This scale is referred to as the Markov-Einstein (ME) scale, and has turned out to be a useful characteristic of complex systems. We provide a review of the operational methods that have been developed for analyzing stochastic data in time and scale. We address in detail the following issues: (i) Reconstruction of stochastic evolution equations from data in terms of the Langevin equations or the corresponding Fokker-Planck equations and (ii) intermittency, cascades and multiscale correlation functions.
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--Irreversibility in response to force acting on the graphene sheets, with N. Abedpour and R. Asgari, Phys. Rev. Lett. 104, 196804 (2010); Phys. Rev. Lett. 106, 209702 (2011) (Pdf)
The amount of rippling in graphene sheets is related to the interactions with the substrate or with the suspending structure. Here, we report on an irreversibility in the response to forces that act on suspended graphene sheets. This may explain why one always observes a ripple structure on suspended graphene. We show that a compression-relaxation mechanism produces static ripples on graphene sheets and determine a peculiar temperature Tc, such that for T < Tc the free energy of the rippled graphene is smaller than that of roughened graphene. We also show that Tc depends on the structural parameters and increases with increasing sample size.
Some movies on “Irreversibility in response to forcing” for different membranes with different boundary conditions: (movies)
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--Stochastic analysis on temperature dependence roughening of amorphous organic films, A. Farahzadi, P. Niyamakom, M. Beigmohammadi, N. Mayer, M. Heuken, F. Ghasemi, M. Reza Rahimi Tabar, T. Michely and M. Wuttig, Europhysics Letters 90, 10008 (2010),(Pdf)
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--Mapping Stochastic Processes onto Complex Networks, with A.H. Shirazi, G.R. Jafari, J. Davoudi, J. Peinke and M. Sahimi, J. Stat. Mech. (2009) P07046 (Pdf)
We introduce a method by which stochastic processes are mapped onto complex networks. As examples, we construct the networks for such time series as those for free-jet and low-temperature helium turbulence, the German stock market index (the DAX), and the white noise. The networks are further studied by contrasting their geometrical properties, such as the mean-length, diameter, clustering, average number of connection per node. By comparing the network properties of the investigated original time series with those for the shuffled and surrogate series, we are able to quantify the effect of the long-range correlations and the fatness of the probability distribution functions of the series on the constructed networks. Most importantly, we demonstrate that the time series can be reconstructed with high precisions by a simple random walk on their corresponding networks. (Pdf)
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--Turbulencelike Behavior of Seismic Time Series, P. Manshour, S. Saberi, M. Sahimi, J. Peinke, Amalio F. Pacheco, M. Reza Rahimi Tabar, Phys. Rev. Lett.102 014101(2009) (Pdf).
Its extended version: Phys. Rev. E 82, 036105 (2010) (Pdf).
A grand challenge in geophysics has been developing a method for predicting when a significant earthquake may occur. Our team has developed a new method that may go a long way towards this goal. Using the method, we analyzed the fluctuations of the detrended increments of the time series for Earth's vertical velocity for many earthquakes. Our analysis reveals a significant change in the nature of the probability density functions (PDF) of the series' increments. For a large earthquake the time at which the PDF undergoes a transition from a Gaussian to a non-Gaussian is 5-10 hours. The key quantity that signals the transition is the parameter Lambda^2 that appears in the PDF. Far from the earthquake, Lambda^2 is almost zero, but close to it suddenly increases, signaling the transition. The PDF's flatness also exhibits the same trends. Therefore the transition in the PDF, and the changes in Lambda^2 and the PDF's flatness, all happening at the same time, represent a new precursor for detecting impending significant earthquakes. A key insight is that, due to localization of elastic waves, only stations close to the epicenter provide the alert.
--The power point presentation:(Pdf)
--Highlight of the paper in the Media
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--The Taylor frozen hypothesis in Burgers turbulence, with A. Bahraminasab, M. D. Niry, J. Davoudi, A. A. Masoudi, and K. R. Sreenivasan, Phys. Rev. E 77 (Rapid Communication) 065302 (2008) (Pdf).
--Analysis of Nonstationary Stochastic Processes with Application to the Fluctuations in the Oil Price, with,F. Ghasemi, M. Sahimi, J. Peinke, R.Friedrich, G. Reza Jafari, M. Reza Rahimi Tabar, Phys. Rev. E 75, (Rapid Communication), 060102(2007) (Pdf)
--Localization of elastic waves in heterogeneous media with off-diagonal disorder and long-range correlations, with, F. Shahbazi, Alireza Bahraminasab,S. Mehdi Vaez Allaei, and Muhammad Sahimi, Phys. Rev. Lett. 94, 165505 (2005) (Pdf)
--Stochastic Analysis and Regeneration of Rough Surfaces, with, G. R. Jafari, S. M. Fazeli, F. Ghasemi, S. M. Vaez Allaei, A. Iraji zad and G. Kavei, Phys. Rev. Lett. 91 (2003) 226101 (Pdf)
--Singularity Time Scale of the Kardar-Parisi-Zhang Equation in the Strong Coupling Limit in 2+1 dimensions, with, F. Shahbazi, and A. A. Masoudi, Journal of Statistical Physics 112, 437 (2003) (Pdf)
--Statistical Theory of the Kardar-Parisi-Zhang Equation in 1+1 Dimension, with, A.A. Masoudi, F. Shahbazi, and J. Davoudi, Phys. Rev. E (65) 026132 (2002)(Pdf)
--Theoretical Model for Kramers-Moyal's description of Turbulence Cascade, with, Jahanshah Davoudi, Phys. Rev. Lett. 82 (1999) 1680 (Pdf)
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Research
Main Research Field
Complex systems, statistical mechanics, stochastic processes and disordered systems
Current Research
* Dynamics of the Complex Systems
* Electronic and mechanical properties of Graphene
* DMD simulation of the proteins in nano-pores
* Stochastic analysis of Heart Interbeat Dynamics
* Stochastic analysis of Epileptic Brain Dynamics
* Monte-Carlo Simulation of Lipids
* Elastic Wave localization
* Seismic time series
* Disordered Systems
* Cascade Models of Fully Developed Turbulence
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