Marios Mattheakis | DeepAI

AI Chat

Featured Co-authors

Stephen Roberts
85 publications
Pavlos Protopapas
40 publications
Marco Brambilla
9 publications
Akshunna S. Dogra
6 publications
Shaan Desai
5 publications
David Sondak
5 publications
Tiago A. E. Ferreira
4 publications
Hayden Joy
3 publications
Blake Bullwinkel
3 publications
Anwesh Bhattacharya
3 publications
Henry Jin
2 publications

∙ 11/08/2022

First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces

Physics-informed neural networks have been widely applied to learn gener...

0 Marios Mattheakis, et al. ∙

∙ 11/01/2022

Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows

Physics-Informed Neural Networks (PINNs) offer a promising approach to s...

0 Raphaël Pellegrin, et al. ∙

∙ 07/12/2022

RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization

Reservoir computers (RCs) are among the fastest to train of all neural n...

0 Hayden Joy, et al. ∙

∙ 10/21/2021

One-Shot Transfer Learning of Physics-Informed Neural Networks

Solving differential equations efficiently and accurately sits at the he...

0 Shaan Desai, et al. ∙

∙ 08/27/2021

Modeling the effect of the vaccination campaign on the Covid-19 pandemic

Population-wide vaccination is critical for containing the SARS-CoV-2 (C...

0 Mattia Angeli, et al. ∙

∙ 08/25/2021

Unsupervised Reservoir Computing for Solving Ordinary Differential Equations

There is a wave of interest in using unsupervised neural networks for so...

0 Marios Mattheakis, et al. ∙

∙ 07/16/2021

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

Accurately learning the temporal behavior of dynamical systems requires ...

0 Shaan Desai, et al. ∙

∙ 06/07/2021

Encoding Involutory Invariance in Neural Networks

In certain situations, Neural Networks (NN) are trained upon data that o...

0 Anwesh Bhattacharya, et al. ∙

∙ 01/15/2021

A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function

The activation function plays a fundamental role in the artificial neura...

0 Tiago A. E. Ferreira, et al. ∙

∙ 10/10/2020

Unsupervised Neural Networks for Quantum Eigenvalue Problems

Eigenvalue problems are critical to several fields of science and engine...

0 Henry Jin, et al. ∙

∙ 10/10/2020

Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread

Studying the dynamics of COVID-19 is of paramount importance to understa...

0 Alessandro Paticchio, et al. ∙

∙ 01/29/2020

Hamiltonian Neural Networks for solving differential equations

There has been a wave of interest in applying machine learning to study ...

0 Marios Mattheakis, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world