Aug. 3, 2022, 1:11 a.m. | Haoran Hu, Connor M Kennedy, Panayotis G. Kevrekidis, Hongkun Zhang

stat.ML updates on arXiv.org arxiv.org

A variety of approaches using compartmental models have been used to study
the COVID-19 pandemic and the usage of machine learning methods with these
models has had particularly notable success. We present here an approach toward
analyzing accessible data on Covid-19's U.S. development using a variation of
the "Physics Informed Neural Networks" (PINN) which is capable of using the
knowledge of the model to aid learning. We illustrate the challenges of using
the standard PINN approach, then how with appropriate …

applications arxiv bio covid covid-19 epidemiology

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US

Research Engineer

@ Allora Labs | Remote

Ecosystem Manager

@ Allora Labs | Remote

Founding AI Engineer, Agents

@ Occam AI | New York