myQuad = Quadrilateral(domainCorners)
numElemU = 10
numElemV = 20
numGauss = 4
maxLevel = 3
phiRefThresh = 0.1
vertex = myQuad.genElemList(numElemU, numElemV)
xPhys, yPhys, wgtsPhys = myQuad.getElemIntPts(vertex, numGauss)
X_f = np.concatenate((xPhys, yPhys, wgtsPhys), axis=1)
hist_f = np.transpose(
np.array([np.zeros((X_f.shape[0]), dtype=np.float32)]))
filename = 'Training_scatter'
scatterPlot(X_f, figHeight, figWidth, filename)
# Generating the prediction mesh
nPred = np.array([[135, 45], [135, 45], [135, 45]])
offset = 2 * model['l']
secBound = np.array(
[[0.0, 0.5 * model['W'] - offset],
[0.5 * model['W'] - offset, 0.5 * model['W'] + offset],
[0.5 * model['W'] + offset, model['W']]],
dtype=np.float32)
Grid, xGrid, yGrid, hist_grid = genGrid(nPred, model['L'], secBound)
filename = 'Prediction_scatter'
scatterPlot(Grid, figHeight, figWidth, filename)
phi_pred_old = hist_grid #Initializing phi_pred_old to zero
numElemEdge = 80
numGaussEdge = 1
xEdge, yEdge, xNormEdge, yNormEdge, wgtsEdge = myAnnulus.getQuadEdgePts(
numElemEdge, numGaussEdge, 4)
trac_x = -model['P'] * xNormEdge
trac_y = -model['P'] * yNormEdge
xEdgePts = np.concatenate((xEdge, yEdge, wgtsEdge, trac_x, trac_y), axis=1)
model_pts = dict()
model_pts['X_int'] = X_f
model_pts['X_bnd'] = xEdgePts
modelNN = PINN_TC(model_data, xEdgePts, NN_param)
filename = 'Training_scatter'
scatterPlot(X_f, xEdgePts, figHeight, figWidth, filename)
nPred = 40
withEdges = [1, 1, 1, 1]
xGrid, yGrid = myAnnulus.getUnifIntPts(nPred, nPred, withEdges)
Grid = np.concatenate((xGrid, yGrid), axis=1)
num_train_its = 1000
numSteps = 4 # Number of refinement steps
for i in range(numSteps):
# Compute/training
start_time = time.time()
modelNN.train(X_f, num_train_its)
elapsed = time.time() - start_time
print('Training time: %.4f' % (elapsed))