Ru Hou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ru Hou has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
Jianzhou Wang
3 shared publications- Andrey G. Cherstvy
University of Potsdam
1 shared publication Chen Wang
1 shared publicationFeng Liu
1 shared publicationJie Wu
1 shared publicationLin Shen
1 shared publication
Latest Publications
- 6 July 2018
- Published by Royal Society of Chemistry (RSC) in Physical Chemistry Chemical Physics
- Vol. 20 (32) , 20827-20848
Biased continuous-time random walks for ordinary and equilibrium cases: facilitation of diffusion, ergodicity breaking and ageing
We examine renewal processes with power-law waiting time distributions (WTDs) and non-zero drift via computing analytically and by computer simulations their ensemble and time averaged spreading characteristics. All possible values of the scaling exponent α are considered for the WTD ψ(t) ∼ 1/t1+α. We treat continuous-time random walks (CTRWs) with 0 < α < 1 for which the mean waiting time diverges, and investigate the behaviour of the process for both ordinary and equilibrium CTRWs for 1 < α < 2 and α > 2. We demonstrate that in the presence of a drift CTRWs with α < 1 are ageing and non-ergodic in the sense of the non-equivalence of their ensemble and time averaged displacement characteristics in the limit of lag times much shorter than the trajectory length. In the sense of the equivalence of ensemble and time averages, CTRW processes with 1 < α < 2 are ergodic for the equilibrium and non-ergodic for the ordinary situation. Lastly, CTRW renewal processes with α > 2—both for the equilibrium and ordinary situation—are always ergodic. For the situations 1 < α < 2 and α > 2 the variance of the diffusion process, however, depends on the initial ensemble. For biased CTRWs with α > 1 we also investigate the behaviour of the ergodicity breaking parameter. In addition, we demonstrate that for biased CTRWs the Einstein relation is valid on the level of the ensemble and time averaged displacements, in the entire range of the WTD exponent α.
- 6 July 2018
- Published by Royal Society of Chemistry (RSC) in Physical Chemistry Chemical Physics
- Vol. 20 (32) , 20827-20848
Biased continuous-time random walks for ordinary and equilibrium cases: facilitation of diffusion, ergodicity breaking and ageing
We examine renewal processes with power-law waiting time distributions (WTDs) and non-zero drift via computing analytically and by computer simulations their ensemble and time averaged spreading characteristics. All possible values of the scaling exponent α are considered for the WTD ψ(t) ∼ 1/t1+α. We treat continuous-time random walks (CTRWs) with 0 < α < 1 for which the mean waiting time diverges, and investigate the behaviour of the process for both ordinary and equilibrium CTRWs for 1 < α < 2 and α > 2. We demonstrate that in the presence of a drift CTRWs with α < 1 are ageing and non-ergodic in the sense of the non-equivalence of their ensemble and time averaged displacement characteristics in the limit of lag times much shorter than the trajectory length. In the sense of the equivalence of ensemble and time averages, CTRW processes with 1 < α < 2 are ergodic for the equilibrium and non-ergodic for the ordinary situation. Lastly, CTRW renewal processes with α > 2—both for the equilibrium and ordinary situation—are always ergodic. For the situations 1 < α < 2 and α > 2 the variance of the diffusion process, however, depends on the initial ensemble. For biased CTRWs with α > 1 we also investigate the behaviour of the ergodicity breaking parameter. In addition, we demonstrate that for biased CTRWs the Einstein relation is valid on the level of the ensemble and time averaged displacements, in the entire range of the WTD exponent α.
- 15 March 2018
- Published by IOP Publishing in Journal of Physics A: Mathematical and Theoretical
- Vol. 51 (15) , 155001
Feynman–Kac equations for reaction and diffusion processes
This paper provides a theoretical framework for deriving the forward and backward Feynman–Kac equations for the distribution of functionals of the path of a particle undergoing both diffusion and reaction processes. Once given the diffusion type and reaction rate, a specific forward or backward Feynman–Kac equation can be obtained. The results in this paper include those for normal/anomalous diffusions and reactions with linear/nonlinear rates. Using the derived equations, we apply our findings to compute some physical (experimentally measurable) statistics, including the occupation time in half-space, the first passage time, and the occupation time in half-interval with an absorbing or reflecting boundary, for the physical system with anomalous diffusion and spontaneous evanescence.
- 15 March 2018
- Published by IOP Publishing in Journal of Physics A: Mathematical and Theoretical
- Vol. 51 (15) , 155001
Feynman–Kac equations for reaction and diffusion processes
This paper provides a theoretical framework for deriving the forward and backward Feynman–Kac equations for the distribution of functionals of the path of a particle undergoing both diffusion and reaction processes. Once given the diffusion type and reaction rate, a specific forward or backward Feynman–Kac equation can be obtained. The results in this paper include those for normal/anomalous diffusions and reactions with linear/nonlinear rates. Using the derived equations, we apply our findings to compute some physical (experimentally measurable) statistics, including the occupation time in half-space, the first passage time, and the occupation time in half-interval with an absorbing or reflecting boundary, for the physical system with anomalous diffusion and spontaneous evanescence.
- 1 December 2016
- Published by Elsevier in Applied Soft Computing
- Vol. 49, 164-178
Improved v -Support vector regression model based on variable selection and brain storm optimization for stock price forecasting
- 1 December 2016
- Published by Elsevier in Applied Soft Computing
- Vol. 49, 164-178
Improved v -Support vector regression model based on variable selection and brain storm optimization for stock price forecasting
- Top Cited Papers
- 1 July 2016
- Published by Elsevier in Renewable and Sustainable Energy Reviews
- Vol. 60, 960-981
Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
- 1 July 2016
- Published by Elsevier in Renewable and Sustainable Energy Reviews
- Vol. 60, 960-981
Top Cited PapersAnalysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models