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A Physics Informed Neural Networks framework to understand the dynamics of COVID-19, governed by the S-I-R-D system of mathematical equations. This focuses on finding the dynamics of COVID, infection, recovery, and death rates; thus, predicting the active infections, recovered, susceptible and deceased timely.

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Determining-COVID-19-Dynamics-Using-Physics-Informed-Neural-Networks

A Physics Informed Neural Networks framework to understand the dynamics of COVID-19, governed by the S-I-R-D system of mathematical equations. This focuses on finding the dynamics of COVID, infection, recovery, and death rates; thus, predicting the active infections, recovered, susceptible and deceased timely.

User Guide Please note that the project was developed using tensorflow 1.x and the optimizer function has a module which is not present in tensorflow 2.x. To solve this problem, please create a new python environment, set it up with python 3.6 or lower.

Then search how to install a version of tensorflow 1.x, this version is not readily available but you can install it using this code ... $ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl ...

(make corresponding changes for Linux)

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A Physics Informed Neural Networks framework to understand the dynamics of COVID-19, governed by the S-I-R-D system of mathematical equations. This focuses on finding the dynamics of COVID, infection, recovery, and death rates; thus, predicting the active infections, recovered, susceptible and deceased timely.

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