51 citations · 77 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…