Table 1 Burgers’ problem: training, validation and testing dataset
From: Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems
Dataset | Samples | Input | Output |
|---|---|---|---|
Training | 1 | \([z^{1},..., z^{n_t-1}, z^{n_t}]\) | \(z^{n_t+1}\) |
2 | \([z^{2},..., z^{n_t}, z^{n_t+1}]\) | \(z^{n_t+2}\) | |
... | ... | ... | |
150 | \([z^{150},..., z^{n_t+148}, z^{n_t+149}]\) | \(z^{n_t+150}\) (training end) | |
Validation | 151 | \([z^{151},..., z^{n_t+149}, z^{n_t+150}]\) | \(z^{n_t+151}\) |
... | ... | ... | |
160 | \([z^{160},..., z^{n_t+158}, z^{n_t+159}]\) | \(z^{n_t+160}\) | |
Testing | 1 | \([z^{1},..., z^{n_t-1}, z^{n_t}]\) | \([z^{n_t+1},..., z^{249}, z^{250}]\) |