  Differential equations play a pivotal role in modern world ranging from
science, engineering, ecology, economics and finance where these can be used to
model many physical systems and processes. In this paper, we study two
mathematical models of a drug assimilation in the human system using Physics
Informed Neural Networks (PINNs). In the first model, we consider the case of
single dose of drug in the human system and in the second case, we consider the
course of this drug taken at regular intervals. We have used the compartment
diagram to model these cases. The resulting differential equations are solved
using PINN, where we employ a feed forward multilayer perceptron as function
approximator and the network parameters are tuned for minimum error. Further,
the network is trained by finding the gradient of the error function with
respect to the network parameters. We have employed DeepXDE, a python library
for PINNs, to solve the simultaneous first order differential equations
describing the two models of drug assimilation. The results show high degree of
accuracy between the exact solution and the predicted solution as much as the
resulting error reaches10^(-11) for the first model and 10^(-8) for the second
model. This validates the use of PINN in solving any dynamical system.
