PHYSICS-INFORMED NEURAL NETWORK (PINN) AND ITS APPLICATION TO POLLUTANT TRANSPORT
Water pollution is one of the environmental problems that can have a significant negative impact on river ecology. To control and address this issue, it is necessary to study the movement of pollutants. The mathematical model of this pollutant movement is formulated in the form of partial differe...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/83041 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Water pollution is one of the environmental problems that can have a significant negative
impact on river ecology. To control and address this issue, it is necessary to study
the movement of pollutants. The mathematical model of this pollutant movement
is formulated in the form of partial differential equations. The advection-diffusion
equation is the mathematical model used in this research. To obtain solutions to this
equation, both analytical methods and the Physics-Informed Neural Network (PINN)
method are employed. Simulations of the advection-diffusion equation results using
both the analytical method and PINN are conducted, and the mean-squared error (MSE)
value is calculated. The simulation results and MSE calculations show that PINN
can be an effective method for solving partial differential equations, particularly the
advection-diffusion equation. Furthermore, after comparing the performance of PINN
against the analytical method, the solution of the two-dimensional advection-diffusion
equation with a pollutant source using the Physics-Informed Neural Network (PINN)
method has been studied. The simulation results indicate that the pollutant will move
in the direction of advection. The concentration of the pollutant will increase and reach
saturation at a certain distance from the pollutant source.
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