117 citations · 534 across the 30 of their papers we have counts for
6 papers · 1 filter
HubbardNet: Efficient Predictions of the Bose-Hubbard Model Spectrum with Deep Neural Networks
Ziyan Zhu, Marios Mattheakis, Weiwei Pan +1
We present a deep neural network (DNN)-based model (HubbardNet) to variationally find the ground state and excited state wavefunctions of the one-dimensional and two-dimensional Bo…
First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces
Marios Mattheakis, Gabriel R. Schleder, Daniel T. Larson +1
Physics-informed neural networks have been widely applied to learn general parametric solutions of differential equations. Here, we propose a neural network to discover parametric…
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis +1
Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sci…
RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization
Hayden Joy, Marios Mattheakis, Pavlos Protopapas
Reservoir computers (RCs) are among the fastest to train of all neural networks, especially when they are compared to other recurrent neural networks. RC has this advantage while s…
Physics-informed neural networks for quantum control
Ariel Norambuena, Marios Mattheakis, Francisco J. González +1
Quantum control is a ubiquitous research field that has enabled physicists to delve into the dynamics and features of quantum systems, delivering powerful applications for various…
Physics-Informed Neural Networks for Quantum Eigenvalue Problems
Henry Jin, Marios Mattheakis, Pavlos Protopapas
Eigenvalue problems are critical to several fields of science and engineering. We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eige…