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

1 code implementation • 1 Nov 2022 • Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis, Pavlos Protopapas

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 sciences.

Transfer Learning

DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks

1 code implementation • 15 Sep 2022 • Blake Bullwinkel, Dylan Randle, Pavlos Protopapas, David Sondak

Solutions to differential equations are of significant scientific and engineering relevance.

Evaluating the Fairness Impact of Differentially Private Synthetic Data

no code implementations • 9 May 2022 • Blake Bullwinkel, Kristen Grabarz, Lily Ke, Scarlett Gong, Chris Tanner, Joshua Allen

Differentially private (DP) synthetic data is a promising approach to maximizing the utility of data containing sensitive information.

Binary Classification Fairness