Solution of fractional Nernst-Plank equation based on deep learning
This paper used fractional physics-informed neural networks(fPINN)to solve the time-fractional equation,and demonstrate its accuracy and effectiveness in solving the forward and inverse problems of time-fractional N-P.Moreover,the paper explain result by analyzing the three sources of numerical errors due to discretization,sampling and optimization.The paper also analyze relative between the discretization and sampling error.The paper find that there exists the best training point set size to minimize the solution error with fixed discretization error.Finally,the paper demonstrate the effectiveness of NN in solving inverse problems.
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