The utilization of concentration distribution measurement data(indirect sampling)to determine the diffusion coefficient and develop an efficient numerical method is an urgent problem in contemporary research.Identifying the diffusion coefficient is inherently an inverse parameter identification problem,typically characterized by ill-posedness,nonlinearity,and high computational complexity.This study investigates the relationship between the diffusion coefficient,concentration,and concentration gradient,while considering the effects of diffusion on material transport at a specified temperature.The diffusion coefficient is computed numerically using dynamic sampling values of material diffusion concentration alongside a multilayer feed-forward neural network.Finally,numerical experiments demonstrate the effectiveness of our proposed method.
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