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pro vyhledávání: '"Pusuluri, Krishna"'
Deep learning models offer superior performance compared to other machine learning techniques for a variety of tasks and domains, but pose their own challenges. In particular, deep learning models require larger training times as the depth of a model
Externí odkaz:
http://arxiv.org/abs/2305.18445
Using the technique of Poincar\'{e} return maps, we disclose an intricate order of the subsequent homoclinics near the primary homoclinic bifurcation of the Shilnikov saddle-focus in systems with reflection symmetry. We also reveal the admissible sha
Externí odkaz:
http://arxiv.org/abs/2104.11314
We study the origin of homoclinic chaos in the classical 3D model proposed by O. R\"ossler in 1976. Of our particular interest are the convoluted bifurcations of the Shilnikov saddle-foci and how their synergy determines the global unfolding of the m
Externí odkaz:
http://arxiv.org/abs/2008.12865
Akademický článek
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Autor:
Pusuluri, Krishna, Shilnikov, Andrey L
Publikováno v:
Phys. Rev. E 98, 040202 (2018)
We developed a powerful computational approach to elaborate on onset mechanisms of deterministic chaos due to complex homoclinic bifurcations in diverse systems. Its core is the reduction of phase space dynamics to symbolic binary representations tha
Externí odkaz:
http://arxiv.org/abs/1806.01309
A suite of analytical and computational techniques based on symbolic representations of simple and complex dynamics, is further developed and employed to unravel the global organization of bi-parametric structures that underlie the emergence of chaos
Externí odkaz:
http://arxiv.org/abs/1806.01306
Publikováno v:
In Communications in Nonlinear Science and Numerical Simulation April 2020 83
Autor:
Pusuluri, Krishna
We study complex behaviors arising in neuroscience and other nonlinear systems by combining dynamical systems analysis with modern computational approaches including GPU parallelization and unsupervised machine learning. To gain insights into the beh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09497ddb39dfabf796b9a3edff3bece5