Mohammad Reza Rahimi Tabar Professor of Physics Sharif University of Technology Tel: +98-212280692, +98-212290934 Fax: +98-216005410 Email: mohammed.r.rahimi.tabar(at)uni-oldenburg.de & rahimitabar(at)sharif.edu --Book Chapters: -- 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) (in press) -- 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:Modeling Critical and Catastrophic Phenomena in Geoscience: A Statistical Physics Approach, Lecture Notes in Physics, 705, pp. 281-301, Springer Verlag, Berlin Heidelberg (2007) (Pdf) _______________________________________________________________________________________________________________ --List of Publications _______________________________________________________________________________________________________________ -- Selected Publications: -- 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) ______________________________________________________________________________________________________________ --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). 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. (Pdf) --The power point presentation: (Pdf) --Highlight of the paper in German Media ______________________________________________________________________________________________________________ --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) 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 My PhD Students Teaching Previous Course Current Course (in Persian) Personal Information Educational Background Academic Responsibility Current Position News ---Events in statistical mechanics and Complex Systems

Last Update Jan. 2009, M.D. Niry