Table 2 Results of Algorithm 1 with gaussian noise added to sparse data for Burgers’ equation.
From: Parameter identification for PDEs using sparse interior data and a recurrent neural network
Gaussianoise | ||||
|---|---|---|---|---|
Results | \(\sigma = 0.01\) | \(\sigma = 0.03\) | \(\sigma = 0.05\) | \(\sigma = 0.1\) |
Relative \(L_{2}\) errors | 6.99e\(-\)3 | 9.13e\(-\)3 | 1.03e\(-\)2 | 3.23e\(-\)2 |
Estimated parameters errors | 4.21e\(-\)2 | 6.42e\(-\)2 | 8.28e\(-\)2 | 1.23e\(-\)1 |
