The timedependent HartreeFock (TDHF) and density functional theory (DFT) are among the most useful approaches within meanfield theories for studying static and dynamic properties of complex manybody systems in different branches of physics. Despite the fact that they provide a good approximation for the average properties of onebody degrees of freedoms, they are known to fail to include quantal fluctuations of collective observables and they do not provide sufficient dissipation of collective motion. In order to incorporate these missing effects the stochastic meanfield (SMF) approach was proposed (Ayik 2008). In the SMF approach a set of stochastic initial onebody densities are evolved. Each stochastic onebody density matrix consists of a set of stochastic Gaussian random numbers that satisfy the first and second moments of collective onebody observables. Recent works indicate that the SMF approach provides a good description of the dynamics of the nuclear systems (Yilmaz et al. 2018; Ayik et al. 2019). In this work, the onedimensional FermiHubbard model is simulated with the SMF approach by using different distributions such as Gaussian, uniform, bimodal and twopoint distributions. The dissipative dynamics are discussed and the predictive power of the SMF approach with different probability distributions are compared with each other and the exact dynamics. As a result it is shown that by considering different distributions, the predictive power of the SMF approach can be improved.
Birincil Dil  en 

Konular  Fen 
Bölüm  Articles 
Yazarlar 

Destekleyen Kurum  TUBITAK 
Proje Numarası  2211A 
Teşekkür  This work was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK). 
Tarihler 
Yayımlanma Tarihi : 27 Aralık 2019 
Bibtex  @konferans bildirisi { beuscitech633558,
journal = {Bitlis Eren University Journal of Science and Technology},
issn = {},
eissn = {21467706},
address = {},
publisher = {Bitlis Eren Üniversitesi},
year = {2019},
volume = {9},
pages = {104  108},
doi = {10.17678/beuscitech.633558},
title = {Dissipative dynamics within stochastic meanfield approach},
key = {cite},
author = {ULGEN, İbrahim and YILMAZ, Bulent}
} 
APA  ULGEN, İ , YILMAZ, B . (2019). Dissipative dynamics within stochastic meanfield approach. Bitlis Eren University Journal of Science and Technology , 9 (2) , 104108 . DOI: 10.17678/beuscitech.633558 
MLA  ULGEN, İ , YILMAZ, B . "Dissipative dynamics within stochastic meanfield approach". Bitlis Eren University Journal of Science and Technology 9 (2019 ): 104108 <https://dergipark.org.tr/tr/pub/beuscitech/issue/50725/633558> 
Chicago  ULGEN, İ , YILMAZ, B . "Dissipative dynamics within stochastic meanfield approach". Bitlis Eren University Journal of Science and Technology 9 (2019 ): 104108 
RIS  TY  JOUR T1  Dissipative dynamics within stochastic meanfield approach AU  İbrahim ULGEN , Bulent YILMAZ Y1  2019 PY  2019 N1  doi: 10.17678/beuscitech.633558 DO  10.17678/beuscitech.633558 T2  Bitlis Eren University Journal of Science and Technology JF  Journal JO  JOR SP  104 EP  108 VL  9 IS  2 SN  21467706 M3  doi: 10.17678/beuscitech.633558 UR  https://doi.org/10.17678/beuscitech.633558 Y2  2019 ER  
EndNote  %0 Bitlis Eren University Journal of Science and Technology Dissipative dynamics within stochastic meanfield approach %A İbrahim ULGEN , Bulent YILMAZ %T Dissipative dynamics within stochastic meanfield approach %D 2019 %J Bitlis Eren University Journal of Science and Technology %P 21467706 %V 9 %N 2 %R doi: 10.17678/beuscitech.633558 %U 10.17678/beuscitech.633558 
ISNAD  ULGEN, İbrahim , YILMAZ, Bulent . "Dissipative dynamics within stochastic meanfield approach". Bitlis Eren University Journal of Science and Technology 9 / 2 (Aralık 2019): 104108 . https://doi.org/10.17678/beuscitech.633558 
AMA  ULGEN İ , YILMAZ B . Dissipative dynamics within stochastic meanfield approach. Bitlis Eren University Journal of Science and Technology. 2019; 9(2): 104108. 
Vancouver  ULGEN İ , YILMAZ B . Dissipative dynamics within stochastic meanfield approach. Bitlis Eren University Journal of Science and Technology. 2019; 9(2): 108104. 