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In the in-situ leaching of uranium, uranium migration is influenced by both groundwater flow and solute diffusion, and this process can be effectively modeled using the advection-diffusion equation. Accurately modeling the variation of uranium concentration over time and space is crucial for predicting uranium migration in groundwater during in-situ leaching of uranium. Traditional numerical methods, such as the finite difference method, are computationally intensive and prone to errors while dealing with high-dimensional, complex problems. Therefore, this research aims to explore the applicability and accuracy of physics-informed neural networks (PINN) in solving the advection-diffusion equation. Through numerical simulations of the one-dimensional advection-diffusion equation, and by comparing the PINN solutions with numerical and analytical solutions. The results show that PINN provide higher accuracy and better alignment with the analytical solution over long-term simulations compared to numerical methods. Furthermore, PINN exhibit certain extrapolation capabilities. Additionally, the introduction of dropout enhances the generalization ability and convergence speed of the PINN model, confirming the potential of PINN in solving complex physical problems.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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