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- W4283771647 abstract "In the 1990s, it became easier for people to obtain digital information and to spread the information through the Internet. Against this background, machine learning has begun to develop vigorously, focusing more on solving practical problems. The phenomenon of radial fluid enhanced diffusion has always been the focus of attention in the field of fluid mechanics. However, due to the limitations of various actual physical conditions, convection dominates the radial fluid enhanced diffusion problem. The classic solution method will cause serious non-physical oscillations when solving the problem, and no more accurate numerical results can be obtained. Based on this, this paper proposes a radial fluid-enhanced diffusion technology based on machine learning. The logarithmic increment method is proposed to improve the non-parametric estimation of the drift coefficient. Experiments show that in the two typical models, the mean value of the logarithmic increment method fluctuates between 0.4–0.6, which is closer to the actual value of 0.495, indicating that the mean value of the logarithmic increment method is closer to the true value and the variance is smaller. The effect is better than that of the direct incremental method." @default.
- W4283771647 created "2022-07-03" @default.
- W4283771647 creator A5051689229 @default.
- W4283771647 date "2022-07-03" @default.
- W4283771647 modified "2023-10-14" @default.
- W4283771647 title "Technology of Radial Fluid Enhanced Diffusion Based on Machine Learning" @default.
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- W4283771647 doi "https://doi.org/10.1007/978-981-19-3632-6_50" @default.
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