You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The network was initially trained assuming constant density and no absorption. Initial tests show that the network may also generalise (even without re-training) to more general cases. This requires updating IterativeSolver.get_residual.
Absorption could possibly be implemented by adding an imaginary part to k_sq, while density gradients could be implemented using, e.g.,
rho_dx=self.Lap.dx(rho)
The additional parameters will need to be propagated through the code, e.g., by adding additional channels to the speed of sound input. This will need changing IterativeSolver.get_initials to only use the sound speed channel in the calculation of k_sq.
The network was initially trained assuming constant density and no absorption. Initial tests show that the network may also generalise (even without re-training) to more general cases. This requires updating
IterativeSolver.get_residual
.Absorption could possibly be implemented by adding an imaginary part to
k_sq
, while density gradients could be implemented using, e.g.,The additional parameters will need to be propagated through the code, e.g., by adding additional channels to the speed of sound input. This will need changing
IterativeSolver.get_initials
to only use the sound speed channel in the calculation ofk_sq
.To:
This will need to propagated in several places that call
get_initials
,get_residual
, or usesos
.The text was updated successfully, but these errors were encountered: