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I could not reproduce the training set you provide when I ran "create_training_data.py" with the default arguments. Is it possible to release the arguments you used for generating all training data and exact data?

And I found that the shape of the training data mismatches your description in the paper: "To train the network we generate a set of 8000 high-resolution solutions to each equation, sampled at regular time intervals from 800 numerical integrations." However, the provided training data of Burger's equation has a shape of [10000, 512] instead of [8000, 512]. Could you explain the difference here?

Since I want to make some modifications based on the equations used in this paper, I want to keep the same parameters for generating data.

Thanks!

The picture is captured from https://github.com/google/data-driven-discretization-1d/blob/master/notebooks/time-integration.ipynb.
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Hi, I run the following command and get a very small file(347 KB).

python create_exact_data.py --output_path burgers_weno.nc

What params should I set to generate a burgers_weno.nc file same as the picture mentioned above?

Hi, I'd like to point out that the instruction pip install related to the project might not be right one. Instead, one should type

pip install -e data-driven-discretization-1d

as it searches the setup.py file inside this folder. Am I wrong? At least this was the only way I succeeded installing the package...

Is there a script for generating results of all baseline methods (Fig S8 of the arXiv version: https://arxiv.org/pdf/1808.04930.pdf)

Now the readme file only illustrates how to generate the result of the neural network model instead of the baselines (1st order, 3rd order, Opt. const, Opt. God. const, WENO).