Results 61 to 70 of about 11,294 (222)

Reinforcement Learning for Jump‐Diffusions, With Financial Applications

open access: yesMathematical Finance, EarlyView.
ABSTRACT We study continuous‐time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump‐diffusion processes. We formulate an entropy‐regularized exploratory control problem with stochastic policies to capture the exploration–exploitation balance essential for RL.
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
wiley   +1 more source

Uncertainty relations in terms of the Tsallis entropy [PDF]

open access: yesPhysical Review A, 2009
Final version accepted in Phys. Rev. A.
Wilk, Grzegorz, Wlodarczyk, Zbigniew
openaire   +2 more sources

What Seismic Phase is L'Aquila in Fifteen Years After the Mw6.1$$ {M}_w\kern0.3em 6.1 $$ Earthquake of April 6, 2009?

open access: yesEnvironmetrics, Volume 37, Issue 3, April 2026.
ABSTRACT On April 6, 2009, central Italy was hit by a Mw6.1$$ {M}_w\kern0.3em 6.1 $$ earthquake that caused 308 victims in the city and province of L'Aquila; subsequently, in 2016, two Mw6+$$ {M}_w\kern0.3em 6+ $$ shocks were recorded in an area located a few dozen kilometers further north, respectively in Amatrice and Norcia.
Elisa Varini   +2 more
wiley   +1 more source

Evaluating Different Methods for Determining the Velocity-Dip Position over the Entire Cross Section and at the Centerline of a Rectangular Open Channel

open access: yesEntropy, 2020
The velocity profile of an open channel is an important research topic in the context of open channel hydraulics; in particular, the velocity-dip position has drawn the attention of hydraulic scientists.
Zhongfan Zhu   +3 more
doaj   +1 more source

Parameterized coherence measure

open access: yesResults in Physics, 2023
Quantifying coherence is an essential endeavor for both quantum mechanical foundations and quantum technologies. We present a bona fide measure of quantum coherence by utilizing the Tsallis relative operator (α,β)-entropy.
Meng-Li Guo   +4 more
doaj   +1 more source

Kaniadakis Holographic Dark Energy Behavior in f(Q) Theory

open access: yesFortschritte der Physik, Volume 74, Issue 4, April 2026.
ABSTRACT In this study, the cosmological behavior of the Kaniadakis holographic dark energy model is investigated under the f(Q)$f(Q)$ theory within the framework of a flat Friedmann–Robertson–Walker (FRW) universe. The generalized holographic energy density based on Kaniadakis entropy is modeled using the Hubble horizon infrared cutoff scale, and the ...
Sinem Kalkan, Can Aktaş
wiley   +1 more source

Loading Non‐Maxwellian Velocity Distributions in Particle Simulations

open access: yesJournal of Geophysical Research: Space Physics, Volume 131, Issue 3, March 2026.
Abstract Numerical procedures for generating non‐Maxwellian velocity distributions in particle simulations are presented. First, Monte Carlo methods for the (r,q) $(r,q)$ distribution that generalizes flattop and Kappa distributions are discussed. Then, two rejection methods for the regularized Kappa distribution are presented, followed by a comparison
Seiji Zenitani   +2 more
wiley   +1 more source

Quantifying Dynamical Complexity of Magnetic Storms and Solar Flares via Nonextensive Tsallis Entropy

open access: yesEntropy, 2011
Over the last couple of decades nonextensive Tsallis entropy has shown remarkable applicability to describe nonequilibrium physical systems with large variability and multifractal structure.
Konstantinos Eftaxias   +5 more
doaj   +1 more source

Pathway Model, Superstatistics, Tsallis Statistics, and a Generalized Measure of Entropy

open access: yes, 2006
The pathway model of Mathai (2005) is shown to be inferable from the maximization of a certain generalized entropy measure. This entropy is a variant of the generalized entropy of order 'alpha', considered in Mathai and Rathie (1975), and it is also ...
A.M. Mathai   +19 more
core   +2 more sources

Quantum Machine Learning Applications to Medical Images: A Survey

open access: yesIET Quantum Communication, Volume 7, Issue 1, January/December 2026.
In this review paper, we provide an outline of quantum neural networks (QNNs), quantum convolution neural networks (QCNNs) and various hybrid models. We also explore human brain‐inspired quantum neuromorphic computing by the quantum spiking neural networks (QSNN).
Mahua Nandy Pal   +2 more
wiley   +1 more source

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