1 citations · 1 across the 5 of their papers we have counts for
6 papers
Phase diagram of the extended chequerboard model
Jiayou Yin, Lu Liu
The chequerboard model was proposed to describe the direct phase transition from the antiferromagnetic (AFM) state to the plaquette-sin glet (PS) solid state observed in SrCu…
Structure-Preserving Dynamic Mode Decomposition for Highly Oscillatory Dynamics of Semiclassical Schrödinger Equations
Yizhe Feng, Weiguo Gao, Jia Yin
We propose two novel data-driven dynamic mode decomposition (DMD)-type methods, the Crank--Nicolson DMD and the semi-implicit DMD, to predict the highly oscillatory dynamics of the…
GPU acceleration of non-equilibrium Green's function calculation using OpenACC and CUDA FORTRAN
Jia Yin, Khaled Z. Ibrahim, Mauro Del Ben +3
The numerical solution of the Kadanoff-Baym nonlinear integro-differential equations, which yields the non-equilibrium Green's functions (NEGFs) of quantum many-body systems, poses…
Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning
Yuanran Zhu, Jia Yin, Cian C. Reeves +2
Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and…
Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation
Ivan Maliyov, Jia Yin, Jia Yao +2
Nonequilibrium dynamics governed by electron-phonon (e-ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitat…
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations
Hardeep Bassi, Yuanran Zhu, Senwei Liang +4
In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-di…