117 citations · 534 across the 30 of their papers we have counts for
31 papers
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…
One-Shot Transfer Learning of Physics-Informed Neural Networks
Shaan Desai, Marios Mattheakis, Hayden Joy +2
Solving differential equations efficiently and accurately sits at the heart of progress in many areas of scientific research, from classical dynamical systems to quantum mechanics.…
Unsupervised Reservoir Computing for Solving Ordinary Differential Equations
Marios Mattheakis, Hayden Joy, Pavlos Protopapas
There is a wave of interest in using unsupervised neural networks for solving differential equations. The existing methods are based on feed-forward networks, {while} recurrent neu…
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems
Shaan Desai, Marios Mattheakis, David Sondak +2
Accurately learning the temporal behavior of dynamical systems requires models with well-chosen learning biases. Recent innovations embed the Hamiltonian and Lagrangian formalisms…
Encoding Involutory Invariances in Neural Networks
Anwesh Bhattacharya, Marios Mattheakis, Pavlos Protopapas
In certain situations, neural networks are trained upon data that obey underlying symmetries. However, the predictions do not respect the symmetries exactly unless embedded in the…
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function
Tiago A. E. Ferreira, Marios Mattheakis, Pavlos Protopapas
The activation function plays a fundamental role in the artificial neural network learning process. However, there is no obvious choice or procedure to determine the best activatio…
Unsupervised Neural Networks for Quantum Eigenvalue Problems
Henry Jin, Marios Mattheakis, Pavlos Protopapas
Eigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for dif…
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
Alessandro Paticchio, Tommaso Scarlatti, Marios Mattheakis +2
Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion wav…
Variational Integrator Graph Networks for Learning Energy Conserving Dynamical Systems
Shaan Desai, Marios Mattheakis, Stephen Roberts
Recent advances show that neural networks embedded with physics-informed priors significantly outperform vanilla neural networks in learning and predicting the long term dynamics o…
Electronic structure calculations of twisted multi-layer graphene superlattices
Georgios A. Tritsaris, Stephen Carr, Ziyan Zhu +6
Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-l…
Hamiltonian neural networks for solving equations of motion
Marios Mattheakis, David Sondak, Akshunna S. Dogra +1
There has been a wave of interest in applying machine learning to study dynamical systems. We present a Hamiltonian neural network that solves the differential equations that gover…
Graphene epsilon-near-zero plasmonic crystals
Marios Mattheakis, Matthias Maier, Wei Xi Boo +1
Plasmonic crystals are a class of optical metamaterials that consist of engineered structures at the sub-wavelength scale. They exhibit optical properties that are not found under…
Physical Symmetries Embedded in Neural Networks
M. Mattheakis, P. Protopapas, D. Sondak +2
Neural networks are a central technique in machine learning. Recent years have seen a wave of interest in applying neural networks to physical systems for which the governing dynam…
Homogenization of plasmonic crystals: Seeking the epsilon-near-zero effect
Matthias Maier, Marios Mattheakis, Efthimios Kaxiras +2
By using an asymptotic analysis and numerical simulations, we derive and investigate a system of homogenized Maxwell's equations for conducting material sheets that are periodicall…
Machine learning with observers predicts complex spatiotemporal behavior
G. Neofotistos, M. Mattheakis, G. D. Barmparis +3
Chimeras and branching are two archetypical complex phenomena that appear in many physical systems; because of their different intrinsic dynamics, they delineate opposite non-trivi…
Emergence and dynamical properties of stochastic branching in the electronic flows of disordered Dirac solids
Marios Mattheakis, G. P. Tsironis, Efthimios Kaxiras
Graphene as well as more generally Dirac solids constitute two dimensional materials where the electronic flow is ultra relativistic. When a Dirac solid is deposited on a different…
Universal behavior of dispersive Dirac cone in gradient-index plasmonic metamaterials
Matthias Maier, Marios Mattheakis, Efthimios Kaxiras +2
We demonstrate analytically and numerically that the dispersive Dirac cone emulating an epsilon-near-zero (ENZ) behavior is a universal property within a family of plasmonic crysta…
Graphene and active metamaterials: theoretical methods and physical properties
Marios Mattheakis, Giorgos P. Tsironis, Efthimios Kaxiras
The interaction of light with matter has triggered the interest of scientists for long time. The area of plasmonics emerges in this context through the interaction of light with va…
Manipulating polarized light with a planar slab of Black Phosphorus
Constantinos A. Valagiannopoulos, Marios Mattheakis, Sharmila N. Shirodkar +1
Wave polarization contains valuable information for electromagnetic signal processing and the ability to manipulate it can be extremely useful in photonic devices. In this work, we…