100 citations · 712 across the 44 of their papers we have counts for
49 papers
Neuronal diversity can improve machine learning for physics and beyond
Anshul Choudhary, Anil Radhakrishnan, John F. Lindner +2
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that lea…
Machine Learning Potential of a Single Pendulum
Swarnendu Mandal, Sudeshna Sinha, Manish Dev Shrimali
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…
Emergent noise-aided logic through synchronization
Manaoj Aravind, P. Parmananda, Sudeshna Sinha
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 operat…
Influence of Allee Effect on Extreme Events in Coupled Three Species Systems
Deeptajyoti Sen, Sudeshna Sinha
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 s…
Enhancement of Extreme Events through the Allee effect and its Mitigation through Noise in a Three Species System
Deeptajyoti Sen, Sudeshna Sinha
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 u…
Forecasting Hamiltonian dynamics without canonical coordinates
Anshul Choudhary, John F. Lindner, Elliott G. Holliday +3
Conventional neural networks are universal function approximators, but because they are unaware of underlying symmetries or physical laws, they may need impractically many training…