scholarly journals A Minimal Dynamical Scheme for “Understanding” Hadronic Widths

1997 ◽  
Vol 45 (5) ◽  
pp. 411-434
Author(s):  
A. N. Mitra ◽  
Anju Sharma
Keyword(s):  
2018 ◽  
Vol 15 (supp01) ◽  
pp. 1840005 ◽  
Author(s):  
Yuri N. Obukhov

We review the basics and the current status of the Poincaré gauge theory of gravity. The general dynamical scheme of Poincaré gauge gravity (PG) is formulated, and its physical consequences are outlined. In particular, we discuss exact solutions with and without torsion, highlight the cosmological aspects, and consider the probing of the spacetime geometry.


1992 ◽  
Vol 03 (supp01) ◽  
pp. 71-77 ◽  
Author(s):  
G. Boffetta ◽  
R. Monasson ◽  
R. Zecchina

A simple dynamical scheme for Attractor Neural Networks with non-monotonic three state effective neurons is discussed. For the unsupervised Hebb learning rule, we give some basic numerical results which are interpreted in terms of a combinatorial task realized by the dynamical process (dynamical selection of optimal subspaces). An analytical estimate of optimal performance is given by resorting to two different simplified versions of the model. We show that replica symmetry breaking is required since the replica symmetric solutions are unstable.


2010 ◽  
Vol 36 (1-2) ◽  
pp. 19-39 ◽  
Author(s):  
Christophe Cassou ◽  
Marie Minvielle ◽  
Laurent Terray ◽  
Claire Périgaud

2010 ◽  
Vol 9 (5) ◽  
pp. 661 ◽  
Author(s):  
Michel Sliwa ◽  
Nicolas Mouton ◽  
Cyril Ruckebusch ◽  
Lionel Poisson ◽  
Abdenacer Idrissi ◽  
...  

2016 ◽  
Vol 144 (12) ◽  
pp. 4591-4603 ◽  
Author(s):  
Michael E. Kozar ◽  
Vasubandhu Misra ◽  
Mark D. Powell

Abstract A new statistical–dynamical scheme is presented for predicting integrated kinetic energy (IKE) in North Atlantic tropical cyclones from a series of environmental input parameters. Predicting IKE is desirable because the metric quantifies the energy across a storm’s entire wind field, allowing it to respond to changes in storm structure and size. As such, IKE is especially useful for quantifying risks in large, low-intensity, high-impact storms such as Sandy in 2012. The prediction scheme, named the Statistical Prediction of Integrated Kinetic Energy, version 2 (SPIKE2), builds upon a previous statistical IKE scheme, by using a series of artificial neural networks instead of more basic linear regression models. By using a more complex statistical scheme, SPIKE2 is able to distinguish nonlinear signals in the environment that could cause fluctuations in IKE. In an effort to evaluate SPIKE2’s performance in a future operational setting, the model is calibrated using archived input parameters from Global Ensemble Forecast System (GEFS) control analyses, and is run in a hindcast mode from 1990 to 2011 using archived GEFS reforecasts. The hindcast results indicate that SPIKE2 performs significantly better than both persistence and climatological benchmarks.


1997 ◽  
Vol 411 (1-2) ◽  
pp. 167-172 ◽  
Author(s):  
D. Delépine ◽  
J.-M. Gérard ◽  
R. González Felipe ◽  
J. Weyers
Keyword(s):  

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