Zobrazeno 1 - 10
of 408
pro vyhledávání: '"Sinha, Sudeshna"'
The quest to understand structure-function relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate
Externí odkaz:
http://arxiv.org/abs/2403.15869
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently
Externí odkaz:
http://arxiv.org/abs/2204.04348
Reservoir Computing offers a great computational framework where a physical system can directly be used as computational substrate. Typically a "reservoir" is comprised of a large number of dynamical systems, and is consequently high-dimensional. In
Externí odkaz:
http://arxiv.org/abs/2201.13390
Publikováno v:
Phys. Rev. E 104, 064207 (2021)
In this article, we present a dynamical scheme to obtain a reconfigurable noise-aided logic gate, that yields all six fundamental 2-input logic operations, including the XOR operation. The setup consists of two coupled bistable subsystems that are ea
Externí odkaz:
http://arxiv.org/abs/2201.07675
Autor:
Sen, Deeptajyoti, Sinha, Sudeshna
We consider the dynamics of two coupled three-species population patches, incorporating the Allee Effect, focussing on the onset of extreme events in the coupled system. First we show that the interplay between coupling and the Allee effect may chang
Externí odkaz:
http://arxiv.org/abs/2110.01192
Autor:
Sen, Deeptajyoti, Sinha, Sudeshna
We consider the dynamics of a three-species system incorporating the Allee Effect, focussing on its influence on the emergence of extreme events in the system. First we find that under Allee effect the regular periodic dynamics changes to chaotic. Fu
Externí odkaz:
http://arxiv.org/abs/2109.05753
Autor:
Roy, Anupama, Sinha, Sudeshna
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena March 2024 180
Autor:
Choudhary, Anshul, Lindner, John F., Holliday, Elliott G., Miller, Scott T., Sinha, Sudeshna, Ditto, William L.
Conventional neural networks are universal function approximators, but because they are unaware of underlying symmetries or physical laws, they may need impractically many training data to approximate nonlinear dynamics. Recently introduced Hamiltoni
Externí odkaz:
http://arxiv.org/abs/2010.15201
We detail how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimensions. A map building perspective elucidates the superiority of Hamiltonia
Externí odkaz:
http://arxiv.org/abs/2008.04214
Autor:
Sinha, Sudeshna
We study the dynamics of coupled systems, ranging from maps supporting chaotic attractors to nonlinear differential equations yielding limit cycles, under different coupling classes, connectivity ranges and initial states. Our focus is the robustness
Externí odkaz:
http://arxiv.org/abs/1912.02025