Python UF Examples

Example #1

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

import numpy as np
pi = np.pi
sin = np.sin
cos = np.cos
exp = np.exp
sh = np.shape
reshape = np.reshape

#%% VarNet import
from Domain import Domain1D
from ADPDE import ADPDE
from UtilityFunc import UF
uf = UF()

sess = tf.Session()

from TF_ELM.base_elm import TF_ELM

#%% 1D AD-PDE: closed-form solution

u = 1.0  # velocity magnitude
D = 0.1 / pi  # diffusivity
T = 2.0  # maximum time bound

tlim = [0, T]  # time limits
# Domain limits:
interval = np.array([-1.0, 1.0])
lim = reshape(interval, [2, 1])

Example #2

import numpy as np

pi = np.pi
sin = np.sin
cos = np.cos
exp = np.exp
shape = np.shape
reshape = np.reshape

from Domain import Domain1D
from ContourPlot import ContourPlot
from ADPDE import ADPDE
from VarNet import VarNet
from UtilityFunc import UF

uf = UF()

import matplotlib.pyplot as plt
from IPython import get_ipython

get_ipython().run_line_magic('matplotlib', 'inline')

#%% PDE input data:

u = 1.0  # velocity magnitude
D = 0.1 / pi  # diffusivity
T = 2.0  # maximum simulation time


# Initial condition:
def IC(x):

Example #3

File: TFModel.py Project: linusec/VarNet

"""

#%% Modules:

import numpy as np
shape = np.shape
reshape = np.reshape
size = np.size

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.python.client import device_lib

from UtilityFunc import UF
uf = UF()

#%% Data Parallelism (Replicated Training)


class TFNN():
    """
    Class to construct the tensorflow computational graph and models accross devices.
    
    Attributes:
        dim
        inpDim
        seqLen
        depth
        layerWidth
        modelId

Example #4

import numpy as np
pi = np.pi
sin = np.sin
cos = np.cos
exp = np.exp
shape = np.shape
reshape = np.reshape

from Domain import Domain1D
from ContourPlot import ContourPlot
from ADPDE import ADPDE
from MOR import MOR
from VarNet import VarNet
from UtilityFunc import UF
uf = UF()

import matplotlib.pyplot as plt
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'inline')

#%% PDE input data:

u = 1.0         # velocity magnitude
T = 2.0         # maximum simulation time

def diffFun(x, t=0, D=0.1/pi):
    """Diffusivity field function."""
    return D*np.ones([shape(x)[0], 1])

def IC(x):

Example #5

functions for a 2D unstructured field which is the solution of the Advection-Diffusion PDE.
Different methods are compared in terms of accuracy and speed. To see the results
simply run the code in a Python console.
For details on the interpolation functions, see the manual at:
    https://docs.scipy.org/doc/scipy/reference/interpolate.html

"""
import time
import numpy as np
import scipy.io as spio
from scipy import interpolate

from Domain import PolygonDomain2D
from ContourPlot import ContourPlot
from UtilityFunc import UF
uf = UF()

import matplotlib.pyplot as plt
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'inline')

#%% Data:

mat = spio.loadmat('coord.mat')
coord = mat['coord']                            # coordinates

# Exact concentration field:
mat = spio.loadmat('cEx.mat')
cEx = mat['cEx']
cEx = cEx.todense()

Example #6

shape = np.shape
reshape = np.reshape
sin = np.sin
cos = np.cos
exp = np.exp
pi = np.pi

from scipy import special
erf = special.erf  # error function

from Domain import PolygonDomain2D
from ContourPlot import ContourPlot
from ADPDE import ADPDE
from VarNet import VarNet
from UtilityFunc import UF
uf = UF()

import matplotlib.pyplot as plt
# from IPython import get_ipython
# get_ipython().run_line_magic('matplotlib', 'inline')

#%% PDE input data:

#lim = np.array([[0, -0.5], [2, 0.5]])  # spatial domain boundaries
T = 1.5  # maximum simulation time
q = [1., 0.]  # velocity magnitude
kappa = 1.e-3  # diffusivity
c0 = 1.0  # concentration value on x=0, |y|<a
a = 0.2  # bounds on the concentration BC
nt = 151  # time discretization number for results