51 citations · 78 across the 6 of their papers we have counts for
5 papers · 1 filter
Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks
Shuheng Liu, Pavlos Protopapas, David Sondak +1
Solving differential equations is a critical challenge across a host of domains. While many software packages efficiently solve these equations using classical numerical approaches…
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks
Blake Bullwinkel, Dylan Randle, Pavlos Protopapas +1
Solutions to differential equations are of significant scientific and engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving…
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…
Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
Dylan Randle, Pavlos Protopapas, David Sondak
Solutions to differential equations are of significant scientific and engineering relevance. Recently, there has been a growing interest in solving differential equations with neur…
Solving Differential Equations Using Neural Network Solution Bundles
Cedric Flamant, Pavlos Protopapas, David Sondak
The time evolution of dynamical systems is frequently described by ordinary differential equations (ODEs), which must be solved for given initial conditions. Most standard approach…