Many-body localization in a long range XXZ model with random-field

2016 ◽  
Vol 502 ◽  
pp. 82-87 ◽  
Author(s):  
Bo Li
Keyword(s):  
Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.


1991 ◽  
Vol 1 (5) ◽  
pp. 647-657 ◽  
Author(s):  
Hans-Aloys Wischmann ◽  
Erwin Müller-Hartmann

2006 ◽  
Vol 51 (3) ◽  
pp. 321-329 ◽  
Author(s):  
B. K. Chakrabarti ◽  
Arnab Das ◽  
Jun-ichi Inoue

1991 ◽  
Vol 66 (25) ◽  
pp. 3281-3284 ◽  
Author(s):  
J. P. Hill ◽  
T. R. Thurston ◽  
R. W. Erwin ◽  
M. J. Ramstad ◽  
R. J. Birgeneau

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