Study of Drug Assimilation in Human System using Physics Informed Neural Networks

• URL: http://arxiv.org/abs/2110.05531v1
• Date: Fri, 8 Oct 2021 07:46:46 GMT
• Title: Study of Drug Assimilation in Human System using Physics Informed Neural Networks
• Authors: Kanupriya Goswami, Arpana Sharma, Madhu Pruthi, Richa Gupta
• Abstract summary: We study two mathematical models of a drug assimilation in the human system using Physics Informed Neural Networks (PINNs) The resulting differential equations are solved using PINN, where we employ a feed forward multilayer perceptron as function approxor and the network parameters are tuned for minimum error. We have employed DeepXDE, a python library for PINNs, to solve the simultaneous first order differential equations describing the two models of drug assimilation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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