2022
DOI: 10.3390/math10152776
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Lie Symmetries of the Nonlinear Fokker-Planck Equation Based on Weighted Kaniadakis Entropy

Abstract: The paper studies the Lie symmetries of the nonlinear Fokker–Planck equation in one dimension, which are associated to the weighted Kaniadakis entropy. In particular, the Lie symmetries of the nonlinear diffusive equation, associated to the weighted Kaniadakis entropy, are found. The MaxEnt problem associated to the weighted Kaniadakis entropy is given a complete solution, together with the thermodynamic relations which extend the known ones from the non-weighted case. Several different, but related, arguments… Show more

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Cited by 7 publications
(7 citation statements)
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“…In this section, we recall some notions and results from [49,50,91,102], concerning the nonlinear Fokker-Planck equation (NFPE), including important examples of entropies, needed for our next discussion in Section 3.…”
Section: Preliminary Notions and Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…In this section, we recall some notions and results from [49,50,91,102], concerning the nonlinear Fokker-Planck equation (NFPE), including important examples of entropies, needed for our next discussion in Section 3.…”
Section: Preliminary Notions and Resultsmentioning
confidence: 99%
“…In our papers [50,91], we generalized these results, for the case of weighted STM, Tsallis and Kaniadakis entropies, respectively. In the final classification of symmetries, the Lie algebras differentiate by means of six "exceptional" values of the Tsallis parameter q and by two "exceptional" values of the Kaniadakis parameter k.…”
Section: Will This Approach Work For Entropy Models As Well ?mentioning
confidence: 85%
See 2 more Smart Citations
“…Among the applications of other entropies (Rényi entropy, Varma entropy, Kaniadakis entropy, relative entropy, weighted entropy, etc. ), we can list the following: Markov chains (see [16][17][18]), model selection (see [19,20]), combinatorics (see [21,22]), finance (see [23][24][25]), Lie symmetries (see [26,27]), and machine learning (see [28,29]).…”
Section: Introductionmentioning
confidence: 99%