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Add an example of Poisson-like 2D problem for multioutput DeepONet Add the example #1549

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Add an example of Poisson-like 2D problem for multioutput DeepONet Add the example #1549

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f236e7c
Add the example
vl-dud Nov 7, 2023
43762c9
Merge branch 'master' into deeponet-multioutput-poisson-example
vl-dud Dec 24, 2023
6600159
Use split_branch strategy
vl-dud Dec 24, 2023
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Use split_branch strategy
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@vl-dud
vl-dud committed Dec 24, 2023
commit 66001594fa6eb7b08df2151db3c039e334822756
18 changes: 7 additions & 11 deletions examples/operator/poisson_aligned_multioutput_pideeponet_2d.py
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Expand Up @@ -2,22 +2,15 @@
Poisson-like 2D problem
Supported backend: tensorflow.compat.v1, tensorflow
"""
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"""Supported backend: tensorflow.compat.v1, tensorflow

Poisson-like 2D problem ... More explanation...
"""

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I'm afraid this is also a fake problem used for demonstration. The equations are of the form dA_xx = f and dB_tt = f, where dA_xx and dB_tt are the second-order derivatives of some A and B with respect to spatial and temporal dimensions, respectively.

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@lululxvi lululxvi Dec 25, 2023 •
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Umm. Is it possible to have a real example?

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Sorry, but the suitable real problem does not come to mind. Do you have any idea?

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A 2D fluid problem with outputs of u and v?

import os

os.environ["DDEBACKEND"] = "tensorflow.compat.v1"

import numpy as np
import deepxde as dde
from deepxde.backend import tf
import matplotlib.pyplot as plt

import deepxde as dde


# Two target variables: A and B
# Equations: dA_xx = f, dB_tt = f
def equation(x, y, f):
A = y[:, 0:1]
B = y[:, 1:2]
# A = y[:, 0:1] and B = y[:, 1:2]
dA_xx = dde.grad.hessian(y, x, component=0, i=0, j=0)
dB_tt = dde.grad.hessian(y, x, component=1, i=1, j=1)
return [dA_xx - f, dB_tt - f]
Expand Down Expand Up @@ -54,13 +47,16 @@ def equation(x, y, f):
num_function=10,
)

# Define DeepONet with two outputs
# Define DeepONet with two outputs.
# Use `split_branch` strategy. The output size of the trunk net is equal
# to the output size of the branch net divided by the number of outputs.
net = dde.nn.DeepONetCartesianProd(
[evaluation_points.shape[0], 100, 100],
[geomtime.dim, 100, 100],
[geomtime.dim, 100, 50],
activation="tanh",
kernel_initializer="Glorot normal",
num_outputs=2,
multi_output_strategy="split_branch",
)

# Train model
Expand Down