| Abstract: |
| In recent years, machine learning techniques, namely, physics informed neural networks (PINNs) are becoming popular to solve various types of differential equations modelling several physical phenomena. Here, we focus the fundamentals of PINNs, and, their performance to solve 1D and 2D singularly perturbed differential equations having two small parameters in the diffusion and convection terms. Further, we study the shortfalls of classical PINNs in solving two-parameter singular perturbation problems, and how to overcome these difficulties through other variants of PINNs. Several numerical experiments are carried out to see their performance. |
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