The presented study focuses on a numerical simulation of the charge transport phenomena inside a solid oxide fuel cell anode. The classical mathematical model leads to a notable discrepancy between measured and predicted overpotentials. One of the possible reasons is the assumption of the constant electrochemical reaction symmetry coefficient. A modified formulation of the problem includes data-driven correction of reaction symmetry coefficient in the electrochemical reaction model. A dedicated computational scheme was developed in which a deep-artificial neural network updates symmetry coefficients depend on operational conditions and available data sets. Neural network was learned on 12 experimental data points of polarization curve of an anode obtained from literature. Learning set contained data for the anode operating in two different temperatures - 800 and 1000 degree Celsius. Test set contained six data points for anode operating in 900 degree Celsius. Coefficients' form proposed by the ANN differ from typically used in the literature. Instead of being independent from temperature and withdrawn current, ANN proposed coefficients that change nonlinearly with these variables. Change of coefficients with temperature and withdrawn current is in accordance with electrochemical laws. The results of predictions are juxtaposed with the experimental data from the literature, giving an excellent agreement and indicating improvement of the mathematical model of SOFC. Mean square error on learning data set was 5.312e-5 and on test data points equal to 4.697e-5. It was shown that such a combined approach could lead to successful prediction of anode overpotential and could be a useful tool in the optimization-design process.