51 citations · 71 across the 5 of their papers we have counts for
11 papers
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…
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…