def create_training_data(nt):
x_train = np.array(create_training_grid(nt))
return x_train
"""Demo to show how to solve 1st order PDE IVP with nnde."""
import numpy as np
from nnde.differentialequation.pde.pde1ivp import PDE1IVP
from nnde.math.trainingdata import create_training_grid
from nnde.neuralnetwork.nnpde1ivp import NNPDE1IVP
if __name__ == '__main__':
# Create training data.
nx = 5
ny = 5
xy_train = np.array(create_training_grid([nx, ny]))
print('The training points are:\n', xy_train)
N = nx * ny
assert len(xy_train) == N
print('A total of %d training points were created.' % N)
# Options for training
training_opts = {}
training_opts['debug'] = True
training_opts['verbose'] = True
training_opts['eta'] = 0.01
training_opts['maxepochs'] = 1000
H = 5
# Test each training algorithm on each equation.
for eq in ('nnde.differentialequation.examples.example_pde1ivp_01', ):
print('Examining %s.' % eq)
pde1ivp = PDE1IVP(eq)
import sys
import numpy as np
from nnde.differentialequation.pde.pde2diff import PDE2DIFF
from nnde.math.trainingdata import create_training_grid
from nnde.neuralnetwork.nnpde2diff import NNPDE2DIFF
if __name__ == "__main__":
# Create training data.
nx = 5
ny = 5
nz = 5
nt = 5
xt_train = np.array(create_training_grid([nx, nt]))
xyt_train = np.array(create_training_grid([nx, ny, nt]))
xyzt_train = np.array(create_training_grid([nx, ny, nz, nt]))
# Options for training
training_opts = {}
training_opts["debug"] = False
training_opts["verbose"] = True
training_opts["eta"] = 0.1
training_opts["maxepochs"] = 1000
training_opts["use_jacobian"] = False
H = 5
# Test each training algorithm on each equation.
for pde in ("nnde.differentialequation.examples.diff1d_halfsine", ):
print("Examining %s." % pde)
"""Demonstration of use of nnde to solve ODE IVP"""
import numpy as np
from nnde.differentialequation.ode.ode1ivp import ODE1IVP
from nnde.math.trainingdata import create_training_grid
from nnde.neuralnetwork.nnode1ivp import NNODE1IVP
if __name__ == '__main__':
# Create training data.
nx = 11
x_train = np.array(create_training_grid(nx))
print('The training points are:\n', x_train)
# Options for training
training_opts = {}
training_opts['debug'] = True
training_opts['verbose'] = True
training_opts['maxepochs'] = 1000
# Test each training algorithm on each equation.
for eq in (
'nnde.differentialequation.examples.lagaris_01',
'nnde.differentialequation.examples.lagaris_02',
):
print('Examining %s.' % eq)
ode1ivp = ODE1IVP(eq)
print(ode1ivp)
# (Optional) analytical solution and derivative