I have noticed from the random_fields.py that the data is generated by sampling from a Gaussian distribution, and then passed to an inverse Fourier transform, therefore I would assume that values of u and v in the generated data to be in the frequency domain, including the truth values computed using the Runge-Kutta method. However, when comparing the output of the neural network and the truth values, it seems that you computed the error using the Frobenius
norm without converting the values back to the time domain.
May I ask whether what domain the generated data is in and why it never passed through a second Fourier transform?
Hi,
I have noticed from the
random_fields.pythat the data is generated by sampling from a Gaussian distribution, and then passed to an inverse Fourier transform, therefore I would assume that values of u and v in the generated data to be in the frequency domain, including the truth values computed using the Runge-Kutta method. However, when comparing the output of the neural network and the truth values, it seems that you computed the error using the Frobenius norm without converting the values back to the time domain.May I ask whether what domain the generated data is in and why it never passed through a second Fourier transform?
Thank you in advance!