3 papers
cond-mat.str-el2025
Transformer-based operator learning framework for self-energy in strongly correlated systems
Yuanran Zhu, Peter Rosenberg, Zhen Huang +3
We introduce -Attention, a Transformer-based operator-learning framework to address a key computational challenge in correlated materials. Our approach utilizes an Encoder-Only…
cs.LG2023
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…
math.DS2020
Learning to predict synchronization of coupled oscillators on randomly generated graphs
Hardeep Bassi, Richard Yim, Rohith Kodukula +3
Suppose we are given a system of coupled oscillators on an unknown graph along with the trajectory of the system during some period. Can we predict whether the system will eventual…