This is the accompanying repository to the paper submission for DAFx20in22, Vienna, Austria by Julian D. Parker, Sebastian J. Schlecht, Rudolf Rabenstein, Maximilian Schäfer. The paper is currently in review.
View the Project on GitHub julian-parker/DAFX22_FNO
Supplemental Material for Physical Modeling using Recurrent Neural Networks with Fast Convolutional Layers
Example 1 - Linear String
Your browser does not support the video tag.
Linear string GRU
Your browser does not support the audio tag.
Linear string Real
Your browser does not support the audio tag.
Linear string Reference
Your browser does not support the audio tag.
Linear string RNN
Your browser does not support the audio tag.
Example 2 - Nonlinear String
Your browser does not support the video tag.
Nonlinear string GRU
Your browser does not support the audio tag.
Nonlinear string Real
Your browser does not support the audio tag.
Nonlinear string Reference
Your browser does not support the audio tag.
Nonlinear string RNN
Your browser does not support the audio tag.
Example 3 - 2D Wave Equation
Your browser does not support the video tag.
Physical parameters used for dataset generation
Linear String
Quantity
Value
\(E\)
5.4 e9
$\rho_s$
1140
$l$
0.65
$A$
0.5188 e-6
$I$
0.171 e-12
$d_1$
8 e-1
$d_3$
1.4 e-5
$T_s$
60.97
$\nu$
50
2d wave equation
Quantity
Value
$l_x$
1
$l_y$
0.95
$c_0$
340
$\rho_o$
1.2041
Nonlinear Tension Modulated String
Quantity
Value
$l$
0.65
$A$
0.5188e-6
$I$
0.171e-12
$\rho$
1140
$E$
5.4e9
$d_1$
1e-2
$d_3$
6e-5
$T_{s0}$
60.97
Network/Training Hyperparameters
Visible in:
train_1d_string.py
train_2d_wave.py
train_1d_nonlinear_string.py