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lululxvi
Specifying boundary points for 2D geometry
I want to specify boundary conditions for 2D geometry like the below (geometry generated by specifying point cloud)
I am unsure how to specify the coordinate points for the two ends of the geometry. On one end , I would like to specify the Dirichlet and Neumann on the other end.
For 1D, I do
bc_l = dde.icbc.DirichletBC(geom, lambda x: 5, boundary_l) bc_r = dde.icbc.NeumannBC(geom, lambda x: 0, boundary_r)
I would like to know if boundary_normal can be used for 2D.
You can use PointSetBC.
The inputs are
PointSetBC(points, values)
Here you can directly input the coordinates and the BCs.
Hi @praksharma , Thanks so much for your reply.
Could you please let me know if there is a similar function for specifying Neumann BC to a set of points (in the source code PointSetBC is mentioned to be applicable for Dirichlet BC)?
Good question. I am not entirely sure, but may be you could use the Operator BC. But I have never used PointSetOperatorBC.
If I you were you, I would try to modify the source code. This might need copying the class PointSetBC and add the capability to compute derivatives by simply copying the normal_derivative function here. This might take 10 mins of coding and debugging.
Anyways, it is better to ask the developer (@lululxvi ) about this. I am just a user like you.
Thanks a lot for the suggestion. I will try PointSetOperatorBC.
@lululxvi , please have a look into this when it's possible for you.
Hi @praksharma and @lululxvi ,
I'm sorry for the late reply. Thank you very much for all the suggestions and help.
I first tried PointSetBC for specifying Dirichlet bc,
"""Backend supported: tensorflow.compat.v1, tensorflow, pytorch, paddle"""
import deepxde as dde
import numpy as np
import matplotlib.pyplot as plt
from deepxde import config
# Backend tensorflow.compat.v1 or tensorflow
from deepxde.backend import tf
# from vedo import *
# from vedo import Points
# from vedo import plotter as vplt
#
# from pprint import pprint
# import matplotlib
# matplotlib.use('TkAgg')
#
# settings.use_parallel_projection = False
def pde(x, y):
dc_t = dde.grad.jacobian(y, x, i=0, j=1)
dc_xx = dde.grad.hessian(y, x, i=0, j=0)
d = 1
return (
dc_t
- d * dc_xx
)
def get_lboundary_points():
"""
"""
lbc_pids = [452, 402, 352, 302, 251, 201, 152, 99, 13, 96]
lbc_vertices = [
[0., 0.9, 0.],
[0., 0.8, 0.],
[0., 0.7, 0.],
[0., 0.6, 0.],
[0., 0.5, 0.],
[0., 0.4, 0.],
[0., 0.3, 0.],
[0., 0.2, 0.],
[0., 0.1, 0.],
[0., 0., 0.]
]
return lbc_vertices
def get_rboundary_points():
"""
"""
rbc_pids = [499, 446, 396, 346, 293, 295, 244, 193, 146, 144]
rbc_vertices = [
[4.9, 0.9, 0.],
[4.9, 0.8, 0.],
[4.9, 0.7, 0.],
[4.9, 0.6, 0.],
[4.9, 0.5, 0.],
[4.9, 0.4, 0.],
[4.9, 0.3, 0.],
[4.9, 0.2, 0.],
[4.9, 0.1, 0.],
[4.9, 0., 0.]
]
return rbc_vertices
def get_points():
pts = [[0., 0., 0.],
[0.1, 0., 0.],
[0.2, 0., 0.],
[0.3, 0., 0.],
[0.4, 0., 0.],
[0.5, 0., 0.],
[0.6, 0., 0.],
[0.7, 0., 0.],
[0.8, 0., 0.],
[0.9, 0., 0.],
[1., 0., 0.],
[1.1, 0., 0.],
[1.2, 0., 0.],
[1.3, 0., 0.],
[1.4, 0., 0.],
[1.5, 0., 0.],
[1.6, 0., 0.],
[1.7, 0., 0.],
[1.8, 0., 0.],
[1.9, 0., 0.],
[2., 0., 0.],
[2.1, 0., 0.],
[2.2, 0., 0.],
[2.3, 0., 0.],
[2.4, 0., 0.],
[2.5, 0., 0.],
[2.6, 0., 0.],
[2.7, 0., 0.],
[2.8, 0., 0.],
[2.9, 0., 0.],
[3., 0., 0.],
[3.1, 0., 0.],
[3.2, 0., 0.],
[3.3, 0., 0.],
[3.4, 0., 0.],
[3.5, 0., 0.],
[3.6, 0., 0.],
[3.7, 0., 0.],
[3.8, 0., 0.],
[3.9, 0., 0.],
[4., 0., 0.],
[4.1, 0., 0.],
[4.2, 0., 0.],
[4.3, 0., 0.],
[4.4, 0., 0.],
[4.5, 0., 0.],
[4.6, 0., 0.],
[4.7, 0., 0.],
[4.8, 0., 0.],
[4.9, 0., 0.],
[0., 0.1, 0.],
[0.1, 0.1, 0.],
[0.2, 0.1, 0.],
[0.3, 0.1, 0.],
[0.4, 0.1, 0.],
[0.5, 0.1, 0.],
[0.6, 0.1, 0.],
[0.7, 0.1, 0.],
[0.8, 0.1, 0.],
[0.9, 0.1, 0.],
[1., 0.1, 0.],
[1.1, 0.1, 0.],
[1.2, 0.1, 0.],
[1.3, 0.1, 0.],
[1.4, 0.1, 0.],
[1.5, 0.1, 0.],
[1.6, 0.1, 0.],
[1.7, 0.1, 0.],
[1.8, 0.1, 0.],
[1.9, 0.1, 0.],
[2., 0.1, 0.],
[2.1, 0.1, 0.],
[2.2, 0.1, 0.],
[2.3, 0.1, 0.],
[2.4, 0.1, 0.],
[2.5, 0.1, 0.],
[2.6, 0.1, 0.],
[2.7, 0.1, 0.],
[2.8, 0.1, 0.],
[2.9, 0.1, 0.],
[3., 0.1, 0.],
[3.1, 0.1, 0.],
[3.2, 0.1, 0.],
[3.3, 0.1, 0.],
[3.4, 0.1, 0.],
[3.5, 0.1, 0.],
[3.6, 0.1, 0.],
[3.7, 0.1, 0.],
[3.8, 0.1, 0.],
[3.9, 0.1, 0.],
[4., 0.1, 0.],
[4.1, 0.1, 0.],
[4.2, 0.1, 0.],
[4.3, 0.1, 0.],
[4.4, 0.1, 0.],
[4.5, 0.1, 0.],
[4.6, 0.1, 0.],
[4.7, 0.1, 0.],
[4.8, 0.1, 0.],
[4.9, 0.1, 0.],
[0., 0.2, 0.],
[0.1, 0.2, 0.],
[0.2, 0.2, 0.],
[0.3, 0.2, 0.],
[0.4, 0.2, 0.],
[0.5, 0.2, 0.],
[0.6, 0.2, 0.],
[0.7, 0.2, 0.],
[0.8, 0.2, 0.],
[0.9, 0.2, 0.],
[1., 0.2, 0.],
[1.1, 0.2, 0.],
[1.2, 0.2, 0.],
[1.3, 0.2, 0.],
[1.4, 0.2, 0.],
[1.5, 0.2, 0.],
[1.6, 0.2, 0.],
[1.7, 0.2, 0.],
[1.8, 0.2, 0.],
[1.9, 0.2, 0.],
[2., 0.2, 0.],
[2.1, 0.2, 0.],
[2.2, 0.2, 0.],
[2.3, 0.2, 0.],
[2.4, 0.2, 0.],
[2.5, 0.2, 0.],
[2.6, 0.2, 0.],
[2.7, 0.2, 0.],
[2.8, 0.2, 0.],
[2.9, 0.2, 0.],
[3., 0.2, 0.],
[3.1, 0.2, 0.],
[3.2, 0.2, 0.],
[3.3, 0.2, 0.],
[3.4, 0.2, 0.],
[3.5, 0.2, 0.],
[3.6, 0.2, 0.],
[3.7, 0.2, 0.],
[3.8, 0.2, 0.],
[3.9, 0.2, 0.],
[4., 0.2, 0.],
[4.1, 0.2, 0.],
[4.2, 0.2, 0.],
[4.3, 0.2, 0.],
[4.4, 0.2, 0.],
[4.5, 0.2, 0.],
[4.6, 0.2, 0.],
[4.7, 0.2, 0.],
[4.8, 0.2, 0.],
[4.9, 0.2, 0.],
[0., 0.3, 0.],
[0.1, 0.3, 0.],
[0.2, 0.3, 0.],
[0.3, 0.3, 0.],
[0.4, 0.3, 0.],
[0.5, 0.3, 0.],
[0.6, 0.3, 0.],
[0.7, 0.3, 0.],
[0.8, 0.3, 0.],
[0.9, 0.3, 0.],
[1., 0.3, 0.],
[1.1, 0.3, 0.],
[1.2, 0.3, 0.],
[1.3, 0.3, 0.],
[1.4, 0.3, 0.],
[1.5, 0.3, 0.],
[1.6, 0.3, 0.],
[1.7, 0.3, 0.],
[1.8, 0.3, 0.],
[1.9, 0.3, 0.],
[2., 0.3, 0.],
[2.1, 0.3, 0.],
[2.2, 0.3, 0.],
[2.3, 0.3, 0.],
[2.4, 0.3, 0.],
[2.5, 0.3, 0.],
[2.6, 0.3, 0.],
[2.7, 0.3, 0.],
[2.8, 0.3, 0.],
[2.9, 0.3, 0.],
[3., 0.3, 0.],
[3.1, 0.3, 0.],
[3.2, 0.3, 0.],
[3.3, 0.3, 0.],
[3.4, 0.3, 0.],
[3.5, 0.3, 0.],
[3.6, 0.3, 0.],
[3.7, 0.3, 0.],
[3.8, 0.3, 0.],
[3.9, 0.3, 0.],
[4., 0.3, 0.],
[4.1, 0.3, 0.],
[4.2, 0.3, 0.],
[4.3, 0.3, 0.],
[4.4, 0.3, 0.],
[4.5, 0.3, 0.],
[4.6, 0.3, 0.],
[4.7, 0.3, 0.],
[4.8, 0.3, 0.],
[4.9, 0.3, 0.],
[0., 0.4, 0.],
[0.1, 0.4, 0.],
[0.2, 0.4, 0.],
[0.3, 0.4, 0.],
[0.4, 0.4, 0.],
[0.5, 0.4, 0.],
[0.6, 0.4, 0.],
[0.7, 0.4, 0.],
[0.8, 0.4, 0.],
[0.9, 0.4, 0.],
[1., 0.4, 0.],
[1.1, 0.4, 0.],
[1.2, 0.4, 0.],
[1.3, 0.4, 0.],
[1.4, 0.4, 0.],
[1.5, 0.4, 0.],
[1.6, 0.4, 0.],
[1.7, 0.4, 0.],
[1.8, 0.4, 0.],
[1.9, 0.4, 0.],
[2., 0.4, 0.],
[2.1, 0.4, 0.],
[2.2, 0.4, 0.],
[2.3, 0.4, 0.],
[2.4, 0.4, 0.],
[2.5, 0.4, 0.],
[2.6, 0.4, 0.],
[2.7, 0.4, 0.],
[2.8, 0.4, 0.],
[2.9, 0.4, 0.],
[3., 0.4, 0.],
[3.1, 0.4, 0.],
[3.2, 0.4, 0.],
[3.3, 0.4, 0.],
[3.4, 0.4, 0.],
[3.5, 0.4, 0.],
[3.6, 0.4, 0.],
[3.7, 0.4, 0.],
[3.8, 0.4, 0.],
[3.9, 0.4, 0.],
[4., 0.4, 0.],
[4.1, 0.4, 0.],
[4.2, 0.4, 0.],
[4.3, 0.4, 0.],
[4.4, 0.4, 0.],
[4.5, 0.4, 0.],
[4.6, 0.4, 0.],
[4.7, 0.4, 0.],
[4.8, 0.4, 0.],
[4.9, 0.4, 0.],
[0., 0.5, 0.],
[0.1, 0.5, 0.],
[0.2, 0.5, 0.],
[0.3, 0.5, 0.],
[0.4, 0.5, 0.],
[0.5, 0.5, 0.],
[0.6, 0.5, 0.],
[0.7, 0.5, 0.],
[0.8, 0.5, 0.],
[0.9, 0.5, 0.],
[1., 0.5, 0.],
[1.1, 0.5, 0.],
[1.2, 0.5, 0.],
[1.3, 0.5, 0.],
[1.4, 0.5, 0.],
[1.5, 0.5, 0.],
[1.6, 0.5, 0.],
[1.7, 0.5, 0.],
[1.8, 0.5, 0.],
[1.9, 0.5, 0.],
[2., 0.5, 0.],
[2.1, 0.5, 0.],
[2.2, 0.5, 0.],
[2.3, 0.5, 0.],
[2.4, 0.5, 0.],
[2.5, 0.5, 0.],
[2.6, 0.5, 0.],
[2.7, 0.5, 0.],
[2.8, 0.5, 0.],
[2.9, 0.5, 0.],
[3., 0.5, 0.],
[3.1, 0.5, 0.],
[3.2, 0.5, 0.],
[3.3, 0.5, 0.],
[3.4, 0.5, 0.],
[3.5, 0.5, 0.],
[3.6, 0.5, 0.],
[3.7, 0.5, 0.],
[3.8, 0.5, 0.],
[3.9, 0.5, 0.],
[4., 0.5, 0.],
[4.1, 0.5, 0.],
[4.2, 0.5, 0.],
[4.3, 0.5, 0.],
[4.4, 0.5, 0.],
[4.5, 0.5, 0.],
[4.6, 0.5, 0.],
[4.7, 0.5, 0.],
[4.8, 0.5, 0.],
[4.9, 0.5, 0.],
[0., 0.6, 0.],
[0.1, 0.6, 0.],
[0.2, 0.6, 0.],
[0.3, 0.6, 0.],
[0.4, 0.6, 0.],
[0.5, 0.6, 0.],
[0.6, 0.6, 0.],
[0.7, 0.6, 0.],
[0.8, 0.6, 0.],
[0.9, 0.6, 0.],
[1., 0.6, 0.],
[1.1, 0.6, 0.],
[1.2, 0.6, 0.],
[1.3, 0.6, 0.],
[1.4, 0.6, 0.],
[1.5, 0.6, 0.],
[1.6, 0.6, 0.],
[1.7, 0.6, 0.],
[1.8, 0.6, 0.],
[1.9, 0.6, 0.],
[2., 0.6, 0.],
[2.1, 0.6, 0.],
[2.2, 0.6, 0.],
[2.3, 0.6, 0.],
[2.4, 0.6, 0.],
[2.5, 0.6, 0.],
[2.6, 0.6, 0.],
[2.7, 0.6, 0.],
[2.8, 0.6, 0.],
[2.9, 0.6, 0.],
[3., 0.6, 0.],
[3.1, 0.6, 0.],
[3.2, 0.6, 0.],
[3.3, 0.6, 0.],
[3.4, 0.6, 0.],
[3.5, 0.6, 0.],
[3.6, 0.6, 0.],
[3.7, 0.6, 0.],
[3.8, 0.6, 0.],
[3.9, 0.6, 0.],
[4., 0.6, 0.],
[4.1, 0.6, 0.],
[4.2, 0.6, 0.],
[4.3, 0.6, 0.],
[4.4, 0.6, 0.],
[4.5, 0.6, 0.],
[4.6, 0.6, 0.],
[4.7, 0.6, 0.],
[4.8, 0.6, 0.],
[4.9, 0.6, 0.],
[0., 0.7, 0.],
[0.1, 0.7, 0.],
[0.2, 0.7, 0.],
[0.3, 0.7, 0.],
[0.4, 0.7, 0.],
[0.5, 0.7, 0.],
[0.6, 0.7, 0.],
[0.7, 0.7, 0.],
[0.8, 0.7, 0.],
[0.9, 0.7, 0.],
[1., 0.7, 0.],
[1.1, 0.7, 0.],
[1.2, 0.7, 0.],
[1.3, 0.7, 0.],
[1.4, 0.7, 0.],
[1.5, 0.7, 0.],
[1.6, 0.7, 0.],
[1.7, 0.7, 0.],
[1.8, 0.7, 0.],
[1.9, 0.7, 0.],
[2., 0.7, 0.],
[2.1, 0.7, 0.],
[2.2, 0.7, 0.],
[2.3, 0.7, 0.],
[2.4, 0.7, 0.],
[2.5, 0.7, 0.],
[2.6, 0.7, 0.],
[2.7, 0.7, 0.],
[2.8, 0.7, 0.],
[2.9, 0.7, 0.],
[3., 0.7, 0.],
[3.1, 0.7, 0.],
[3.2, 0.7, 0.],
[3.3, 0.7, 0.],
[3.4, 0.7, 0.],
[3.5, 0.7, 0.],
[3.6, 0.7, 0.],
[3.7, 0.7, 0.],
[3.8, 0.7, 0.],
[3.9, 0.7, 0.],
[4., 0.7, 0.],
[4.1, 0.7, 0.],
[4.2, 0.7, 0.],
[4.3, 0.7, 0.],
[4.4, 0.7, 0.],
[4.5, 0.7, 0.],
[4.6, 0.7, 0.],
[4.7, 0.7, 0.],
[4.8, 0.7, 0.],
[4.9, 0.7, 0.],
[0., 0.8, 0.],
[0.1, 0.8, 0.],
[0.2, 0.8, 0.],
[0.3, 0.8, 0.],
[0.4, 0.8, 0.],
[0.5, 0.8, 0.],
[0.6, 0.8, 0.],
[0.7, 0.8, 0.],
[0.8, 0.8, 0.],
[0.9, 0.8, 0.],
[1., 0.8, 0.],
[1.1, 0.8, 0.],
[1.2, 0.8, 0.],
[1.3, 0.8, 0.],
[1.4, 0.8, 0.],
[1.5, 0.8, 0.],
[1.6, 0.8, 0.],
[1.7, 0.8, 0.],
[1.8, 0.8, 0.],
[1.9, 0.8, 0.],
[2., 0.8, 0.],
[2.1, 0.8, 0.],
[2.2, 0.8, 0.],
[2.3, 0.8, 0.],
[2.4, 0.8, 0.],
[2.5, 0.8, 0.],
[2.6, 0.8, 0.],
[2.7, 0.8, 0.],
[2.8, 0.8, 0.],
[2.9, 0.8, 0.],
[3., 0.8, 0.],
[3.1, 0.8, 0.],
[3.2, 0.8, 0.],
[3.3, 0.8, 0.],
[3.4, 0.8, 0.],
[3.5, 0.8, 0.],
[3.6, 0.8, 0.],
[3.7, 0.8, 0.],
[3.8, 0.8, 0.],
[3.9, 0.8, 0.],
[4., 0.8, 0.],
[4.1, 0.8, 0.],
[4.2, 0.8, 0.],
[4.3, 0.8, 0.],
[4.4, 0.8, 0.],
[4.5, 0.8, 0.],
[4.6, 0.8, 0.],
[4.7, 0.8, 0.],
[4.8, 0.8, 0.],
[4.9, 0.8, 0.],
[0., 0.9, 0.],
[0.1, 0.9, 0.],
[0.2, 0.9, 0.],
[0.3, 0.9, 0.],
[0.4, 0.9, 0.],
[0.5, 0.9, 0.],
[0.6, 0.9, 0.],
[0.7, 0.9, 0.],
[0.8, 0.9, 0.],
[0.9, 0.9, 0.],
[1., 0.9, 0.],
[1.1, 0.9, 0.],
[1.2, 0.9, 0.],
[1.3, 0.9, 0.],
[1.4, 0.9, 0.],
[1.5, 0.9, 0.],
[1.6, 0.9, 0.],
[1.7, 0.9, 0.],
[1.8, 0.9, 0.],
[1.9, 0.9, 0.],
[2., 0.9, 0.],
[2.1, 0.9, 0.],
[2.2, 0.9, 0.],
[2.3, 0.9, 0.],
[2.4, 0.9, 0.],
[2.5, 0.9, 0.],
[2.6, 0.9, 0.],
[2.7, 0.9, 0.],
[2.8, 0.9, 0.],
[2.9, 0.9, 0.],
[3., 0.9, 0.],
[3.1, 0.9, 0.],
[3.2, 0.9, 0.],
[3.3, 0.9, 0.],
[3.4, 0.9, 0.],
[3.5, 0.9, 0.],
[3.6, 0.9, 0.],
[3.7, 0.9, 0.],
[3.8, 0.9, 0.],
[3.9, 0.9, 0.],
[4., 0.9, 0.],
[4.1, 0.9, 0.],
[4.2, 0.9, 0.],
[4.3, 0.9, 0.],
[4.4, 0.9, 0.],
[4.5, 0.9, 0.],
[4.6, 0.9, 0.],
[4.7, 0.9, 0.],
[4.8, 0.9, 0.],
[4.9, 0.9, 0.]]
return pts
def model(gdata):
geom = dde.geometry.PointCloud(gdata)
timedomain = dde.geometry.TimeDomain(0, 60)
geomtime = dde.geometry.GeometryXTime(geom, timedomain)
# left_bc = dde.DirichletBC(geomtime, func_lbc, lambda _, on_boundary: on_boundary)
# right_bc = dde.NeumannBC(geomtime, func_rbc, lambda _, on_boundary: on_boundary)
# bc_left = dde.DirichletBC(geomtime, lambda x: 0, lambda x, _: np.isclose(x[0], 0))
# bc_right = dde.DirichletBC(geomtime, lambda x: 1, lambda x, _: np.isclose(x[0], 1))
lbc_vertices = get_lboundary_points()
lbc_pts = np.array(lbc_vertices, dtype=config.real(np))
lbc_values = np.array([5]*len(lbc_vertices), dtype=config.real(np))
bc_l = dde.icbc.PointSetBC(lbc_pts, lbc_values, component=0)
# rbc_vertices = get_rboundary_points()
# rbc_pts = np.array(rbc_vertices, dtype=config.real(np))
# rbc_values = [1]*len(rbc_vertices)
# bc_r = dde.icbc.PointSetBC(rbc_pts, rbc_values, component=0)
ic = dde.icbc.IC(geomtime, lambda x: 1, lambda _, on_initial: on_initial, component=0)
data = dde.data.TimePDE(
geomtime,
pde,
[bc_l, ic],
num_domain=320,
num_test=80000
)
layer_size = [2] + [30] * 6 + [1]
activation = "tanh"
initializer = "Glorot uniform"
net = dde.nn.FNN(layer_size, activation, initializer)
model = dde.Model(data, net)
model.compile("adam", lr=1e-3, metrics=["l2 relative error"])
losshistory, train_state = model.train(iterations=200)
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
if __name__ == '__main__':
pts = get_points()
gdata = np.array(pts)
model(gdata=gdata)
# pcloud = Points(pts, c='k', alpha=0.5)
# vplt.show(pcloud)
Unfortunately, there is a concat error
Traceback (most recent call last):
File "I:\xxx\xxx\simdeep_2d_pipe.py", line 624, in <module>
model(gdata=gdata)
File "I:\xxx\xxx\simdeep_2d_pipe.py", line 600, in model
data = dde.data.TimePDE(
^^^^^^^^^^^^^^^^^
File "I:\xxxx\venv\Lib\site-packages\deepxde\data\pde.py", line 322, in __init__
super().__init__(
File "I:\xxxx\venv\Lib\site-packages\deepxde\data\pde.py", line 127, in __init__
self.train_next_batch()
File "I:\xxxx\venv\Lib\site-packages\deepxde\utils\internal.py", line 38, in wrapper
return func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "I:\xxxx\venv\Lib\site-packages\deepxde\data\pde.py", line 176, in train_next_batch
self.bc_points() # Generate self.num_bcs and self.train_x_bc
^^^^^^^^^^^^^^^^
File "I:\xxxx\venv\Lib\site-packages\deepxde\utils\internal.py", line 38, in wrapper
return func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "I:\xxxx\venv\Lib\site-packages\deepxde\data\pde.py", line 285, in bc_points
np.vstack(x_bcs)
File "<__array_function__ internals>", line 200, in vstack
File "I:\xxxx\venv\Lib\site-packages\numpy\core\shape_base.py", line 296, in vstack
return _nx.concatenate(arrs, 0, dtype=dtype, casting=casting)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<__array_function__ internals>", line 200, in concatenate
ValueError: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 3 and the array at index 1 has size 4
I am not sure how to fix the error, I double checked the size of lbc_pts and lbc_values . It is of same length.
lbc_vertices = get_lboundary_points()
lbc_pts = np.array(lbc_vertices, dtype=config.real(np))
lbc_values = np.array([5]*len(lbc_vertices), dtype=config.real(np))
bc_l = dde.icbc.PointSetBC(lbc_pts, lbc_values, component=0)
I couldn't figure out the source of the error. The geometry is a rectangular domain, pts includes the coordinates of points in the domain. get_lboundary_points function returns the coordinates of the points that lie on the left boundary of the domain.
Could you please have a look when it is possible for you?
Prof. @lululxvi ,
Sorry for the really late post.
I tried to convert to a column vector of the following form
lbc_values = np.array([5] * len(lbc_vertices), dtype=config.real(np)).reshape(-1, 1)
But I get the same error unfortunately
Hello @DeepaMahm, did you manage to implement NeumannBC on a Pointcloud geometry ? If you did I would be glad to ask you a few questions about it.
Thanks, Greg
Hi @Gregouare,
To set up the NeumannBC on a pointcloud, I computed the boundary normals and assigned it to the PointCloud function.
geom = dde.geometry.PointCloud(
points=np.asarray(domain_points),
# boundary_points=np.asarray(lbc_points),
boundary_points=boundary_points,
boundary_normals=boundary_normals
)
For a simply geometry like a rectangle, we could do the following to define NeumannBC.
top_bc = dde.icbc.NeumannBC(geomtime, func_neumann_bc, boundary_top)
def boundary_top(x, on_boundary):
"""
NC boundary for model_v1
:param x:
:param on_boundary:
:return:
"""
return on_boundary and np.isclose(x[1], 5)
I am not sure, how to set this up for a complex geometry defined using pointcloud. If you have some thoughts, I would like to discuss.
Hi, thanks for the quick answer !
So far I have been trying to set up a dummy 3D case of a cube of size one, which has but one non zero heat flux face. (So that I can compare with the same network built with the geometry.cuboid class.
Here is a snippet for the implementation of the geom and BCs :
geom = dde.geometry.PointCloud(points = X_, boundary_points = boundary_p,
boundary_normals = boundary_n)
timedomain = dde.geometry.TimeDomain(0, 1)
geomtime = dde.geometry.GeometryXTime(geom, timedomain)
def boundary_x_in(x, on_boundary):
return (on_boundary and np.isclose(x[0], -l))
def boundary_x_out(x, on_boundary):
return (on_boundary and np.isclose(x[0], l))
def boundary_y_in(x, on_boundary):
return (on_boundary and np.isclose(x[1], -l))
def boundary_y_out(x, on_boundary):
return (on_boundary and np.isclose(x[1], l))
def boundary_z_in(x, on_boundary):
return (on_boundary and np.isclose(x[2], -l))
def boundary_z_out(x, on_boundary):
return (on_boundary and np.isclose(x[2], l))
### Initial and boundary conditions:
bc_x_in = dde.icbc.NeumannBC(geomtime, lambda X: 1 , boundary_x_in)
bc_x_out = dde.icbc.NeumannBC(geomtime, lambda X: 0, boundary_x_out)
bc_y_in = dde.icbc.NeumannBC(geomtime, lambda X: 0, boundary_y_in)
bc_y_out = dde.icbc.NeumannBC(geomtime, lambda X: 0, boundary_y_out)
bc_z_in = dde.icbc.NeumannBC(geomtime, lambda X: 0, boundary_z_in)
bc_z_out = dde.icbc.NeumannBC(geomtime, lambda X: 0, boundary_z_out)
But when it comes to the compilation, I have the error message : "NotImplementedError: PointCloud.boundary_normal to be implemented".
Could you maybe help me with the format of boundary_normals ? Or is the problem coming from another part of the code (it happens during the compilation) ? Then I will try a more complex geometry, still 3D. But if you have questions do not hesitate maybe I will be able to help a bit !
@Gregouare You are probably using an older version of DeepXDE. The PointCloud class has boundary_normal defined. https://github.com/lululxvi/deepxde/blob/3810a9888d13e04cecf114e93a00dfbf90021472/deepxde/geometry/pointcloud.py#L65
Depending on whether you are using conda or just python virtualenv the upgrade procedure may vary. You can take help of ChatGPT to upgrade DeepXDE, as they just launched free GPT4 for public yesterday.:)
Additionally, please share the entire error traceback. Providing just "NotImplementedError: PointCloud.boundary_normal to be implemented" isn't very helpful as it requires us to make many assumptions.
@Gregouare , boundary normals for 2D looks like: https://github.com/lululxvi/deepxde/issues/1688#issuecomment-2028288049
[ [-1, 0], [-1, 0], [-1, 0], ]
As @praksharma pointed out,
The normals are undefined on the corners, so just exclude those points. Mathematically, the derivative is undefined on corners. https://github.com/lululxvi/deepxde/issues/1688#issuecomment-2028379855
Understood, I am going to look into it and see whether it is a version problem (which it seems) or not . Sorry for the partial Traceback by the way, I thought it wouldn't be needed.
@DeepaMahm Sounds good, that is how I defined it. One last question regarding this matter for the moment : I already excluded the normal vectors corresponding to the points on the edges of the domain, can you confirm that I also have to delete those points from the points defining the domain before passing them into dde.geometry.PointCloud ?
Thanks a lot for your help !
@Gregouare ,
Yes, in the below, points excludes the boundary_points
geom = dde.geometry.PointCloud(
points=np.asarray(domain_points),
boundary_points=boundary_points,
boundary_normals=boundary_normals
)
My bad, I actually meant to exclude the edges points from boundary points... But in order to have the arrays (boundary_points & boundary_normals) of same size it seems natural to do so. Thanks a lot for your quick answers, I will try and let you know if the code runs and converges in 3D.