activity
20182020
most citedCoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems

10 citations · 18 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2020★ 10 cited

CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems

Mohannad Elhamod, Jie Bu, Christopher Singh +5

Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network out…

cond-mat.mtrl-sci2020★ 8 cited

Correlation induced emergent charge order in metallic vanadium dioxide

Christopher N. Singh, L. F. J Piper, Hanjong Paik +2

Recent progress in growth and characterization of thin-film VO has shown its electronic properties can be significantly modulated by epitaxial matching. To throw new light on t…

cond-mat.mes-hall2019

Quantum-statistical transport phenomena in memristive computing architectures

Christopher N. Singh, Brian A. Crafton, Mathew P. West +8

The advent of reliable, nanoscale memristive components is promising for next generation compute-in-memory paradigms, however, the intrinsic variability in these devices has preven…

cond-mat.str-el2018

Cooperative Effects of Strain and Electron Correlation in Epitaxial VO2 and NbO2

Wei-Cheng Lee, Matthew J. Wahila, Shantanu Mukherjee +9

We investigate the electronic structure of the epitaxial VO films in the rutile phase using the density functional theory combined with the slave spin method (DFT+SS). In DFT-S…

cond-mat.str-el2018

Importance of orbital fluctuations for the magnetic dynamics in heavy-fermion compound SmB

Christopher N. Singh, Wei-Cheng Lee

The emergent dynamical processes associated with magnetic excitations in heavy-fermion SmB are investigated. By imposing multiorbital interactions on a first-principles model,…