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Table 11 The comparison of PINNs results with the exact solution for different values of time variable t to Example 4.3

From: A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions

t

\(\text{ L}_{2}\)

\(\text{ L}_{\infty }\)

RMSE

0.0

\(2.380\times 10^{-7}\)

\(9.994\times 10^{-7}\)

\(7.124\times 10^{-7}\)

0.1

\(2.585\times 10^{-7}\)

\(1.974\times 10^{-6}\)

\(7.735\times 10^{-7}\)

0.2

\(1.881\times 10^{-6}\)

\(3.188\times 10^{-6}\)

\(2.691\times 10^{-6}\)

0.3

\(3.251\times 10^{-6}\)

\(3.867\times 10^{-6}\)

\(2.779\times 10^{-6}\)

0.4

\(1.046\times 10^{-6}\)

\(2.501\times 10^{-6}\)

\(1.496\times 10^{-6}\)

0.5

\(4.556\times 10^{-7}\)

\(1.247\times 10^{-6}\)

\(3.894\times 10^{-7}\)