Abstract
In present research a neural network-based method named as ‘Radial basis function neural network method’ is tested upon two-dimensional Burgers’ equation. For this purpose, five types of the radial basis functions are considered such as; Linear, Cubic, Quintic, Multiquadric, and gaussian. The validation of results is provided via L∞ error, Mean Square Error, and the Root Mean Square Error. The obtained errors are acceptable from mathematical view point. The graphical compatibility of the results is also tested via the comparison of exact and predicted solutions in the graphs. It is a novel viewpoint to tackle the complex natured mathematical problems. In such method there is no discretization, linearization or quasi-linearization is provided. Therefore, no such scope of error is present. The present method will surely be a major breakthrough to tackle numerous mathematical models.
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