# Application of Physics-Informed Neural Networks to River Sifting Simulation
## 4 Discussion  
PINN could be used for the prediction of fluid flow in several different contexts such as water supply, oil wells, etc. Moreover, the ability to adapt to different conditions and changing parameters makes it possible to tune and learn from different datasets and changes for each new scenario. Due to the versatility of the PINN model, it can be applied in various cases, and the results confirm its high accuracy and reliability in different application areas. In this discussion, we presented a comparative analysis of the physics-informed neural network method and classical numerical simulation techniques for solving problems related to the simulation of physical processes. Both approaches have their advantages and limitations, and understanding their differences is crucial in selecting the most suitable method for a given problem. In contrast, classical numerical simulation relies on solving differential equations that describe physical processes through numerical integration methods. This approach has a well-established foundation in scientific computing and is widely used in various fields. Numerical simulations are highly accurate when appropriate numerical methods and parameters are employed. However, they can be computationally intensive, especially for complex problems or high-resolution simulations. Table 2 provides a comparative analysis of the time performance of the PINN method and numerical simulation, measured in seconds and the number of elements or parameters involved. The results demonstrate that the PINN method requires 1948 s with 4833 parameters of neural networks (weights and biases), while Ansys simulation takes 1080 s with 50,300 elements, 780 s with 30,000 elements, and 240 s with 4800 elements. It is important to note that the PINN model was trained for 7500 epochs on a CPU resource, matching the computational platform used for Ansys simulation. However, it is important to consider the following factors: (a) the training time of the PINN method may depend on the complexity of the task, the amount of training data, and the resources used for training; (b) the execution time of numerical modeling in Ansys Fluent depends on the grid size, geometry complexity, the chosen numerical methods, and computational resources. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Time (s)** & **Element Numbers or Parameters** \\ \hline PINN & 1948 & 4833 \\ \hline \multirow{3}{*}{Ansys} & 1080 & 50,300 \\ \cline{2-3}  & 780 & 30,000 \\ \cline{2-3}  & 240 & 4800 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time comparison of the PINN method and numerical simulation. The choice between the PINN method and numerical simulation depends on several factors, including the availability of data, problem complexity, and computational resources. While the PINN method may offer computational advantages, its accuracy may be compromised in cases with insufficient or inaccurate data. Additionally, training the neural network under certain physical conditions can be challenging. On the other hand, numerical simulation excels in terms of accuracy but may require substantial computational resources, making it less efficient for some applications. We investigated the fact that siltation affects the density of a fluid, and this change can be monitored using various density parameters. In addition, by using flow rate analysis, we can implicitly detect the presence of a blockage. The main contribution of this work is integrating PINN for a more reliable and accurate method of clog identification compared to previous simulation methods. In conclusion, both the PINN method and numerical simulation play vital roles in simulating physical processes. Researchers and engineers must carefully evaluate the specific requirements and constraints of their problems to determine the most suitable approach. Furthermore, ongoing advancements in computational methods, including the integration of neural networks, continue to expand the possibilities for solving complex physical problems efficiently and accurately.