100 citations · 165 across the 8 of their papers we have counts for
10 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…
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…
Mastering high-dimensional dynamics with Hamiltonian neural networks
Scott T. Miller, John F. Lindner, Anshul Choudhary +2
We detail how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimension…
Weak-winner phase synchronization: A curious case of weak interactions
Anshul Choudhary, Arindam Saha, Samuel Krueger +4
We report the observation of a novel and non-trivial synchronization state in a system consisting of three oscillators coupled in a linear chain. For certain ranges of coupling str…
Recovery time after localized perturbations in complex dynamical networks
Chiranjit Mitra, Tim Kittel, Anshul Choudhary +2
Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems op…
Suppression and Revival of Oscillations through Time-varying Interaction
Sudhanshu Shekhar Chaurasia, Anshul Choudhary, Manish Dev Shrimali +1
We explore the dynamical consequences of switching the coupling form in a system of coupled oscillators. We consider two types of switching, one where the coupling function changes…