-
Notifications
You must be signed in to change notification settings - Fork 2
/
epnn_module_utility.py
184 lines (137 loc) · 5.96 KB
/
epnn_module_utility.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
""" Elasto-Plastic Neural Network (EPNN)
DEVELOPED AT:
COMPUTATIONAL GEOMECHANICS LABORATORY
DEPARTMENT OF CIVIL ENGINEERING
UNIVERSITY OF CALGARY, AB, CANADA
DIRECTOR: Prof. Richard Wan
DEVELOPED BY:
MAHDAD EGHBALIAN
MIT License
Copyright (c) 2022 Mahdad Eghbalian
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import torch
import pickle
import numpy as np
def data_loader_pt(file_name):
return torch.load(file_name)
def data_loader_dat(file_name):
get_data = pickle.load(open(file_name, "rb"))
return get_data
def data_dumper_dat(file_name, outputset):
pickle.dump(outputset, open(file_name, "wb"))
return
def pred_error(n11, n12, n2, data1, data2, criterion, min1, min2, range1, range2, min3, range3, device, param):
n11.eval()
n12.eval()
n2.eval()
# if normalized in [-1, 1]
coeff1 = 2.0
coeff2 = 1.0
# if normalized in [0, 1]
# coeff1 = 1.0
# coeff2 = 0.0
input11 = n11.forward(data1.x)
input12 = n12.forward(data1.x)
target11 = data1.y[:, 0:1]
target12 = data1.y[:, 1:4]
out11 = 100.0 * (torch.norm(input11 - target11) / torch.norm(target11))
out12 = 100.0 * (torch.norm(input12 - target12) / torch.norm(target12))
input1m = torch.cat((input11, data1.x[:, [0, 1, 2, 3, 7, 8, 9, 10, 11, 12]]), 1)
input21 = n2.forward(input1m)
# constant tensors
# oneten1 = torch.ones(3, 1, dtype=torch.float32)
oneten1 = torch.ones(3, 1).double()
oneten1 = oneten1.to(device)
# oneten2 = torch.ones(data2.y.shape[0], data2.y.shape[1], dtype=torch.float32)
oneten2 = torch.ones(data2.y.shape[0], data2.y.shape[1]).double()
oneten2 = oneten2.to(device)
dstrain = data1.x[:, 10:]
dstrain_real = torch.mul(dstrain + coeff2, range3) / coeff1 + min3
# dstrainpl = input12 # predicted plastic strain increment
dstrainpl = target12 # actual plastic strain increment
dstrainpl_real = torch.mul(dstrainpl + coeff2, range1[1:4]) / coeff1 + min1[1:4]
dstrainel = dstrain_real - dstrainpl_real
dstrainelv = torch.matmul(dstrainel, oneten1)
dstrainelvten = torch.mul(dstrainelv, oneten2)
mu = torch.mul(param, input21[:, 0:1])
input22 = 2.0 * torch.mul(mu, dstrainel)
input23 = torch.mul((input21[:, 0:1] - (2.0 / 3.0) * mu), dstrainelvten)
input24 = input22 + input23
input2 = coeff1 * torch.div((input24 - min2), range2) - coeff2
target2 = data2.y
out2 = 100.0 * (torch.norm(input2 - target2) / torch.norm(target2))
n11.train()
n12.train()
n2.train()
return out11.item(), out12.item(), out2.item()
def cost_function(n11, n12, n2, data1, data2, criterion, min1, min2, range1, range2, min3, range3, device, param):
# if normalized in [-1, 1]
coeff1 = 2.0
coeff2 = 1.0
# if normalized in [0, 1]
# coeff1 = 1.0
# coeff2 = 0.0
input11 = n11.forward(data1.x)
input12 = n12.forward(data1.x)
target11 = data1.y[:, 0:1]
target12 = data1.y[:, 1:4]
cost11 = criterion(input11, target11)
cost12 = criterion(input12, target12)
input1m = torch.cat((input11, data1.x[:, [0, 1, 2, 3, 7, 8, 9, 10, 11, 12]]), 1)
input21 = n2.forward(input1m)
# constant tensors
# oneten1 = torch.ones(3, 1, dtype=torch.float32)
oneten1 = torch.ones(3, 1).double()
oneten1 = oneten1.to(device)
# oneten2 = torch.ones(data2.y.shape[0], data2.y.shape[1], dtype=torch.float32)
oneten2 = torch.ones(data2.y.shape[0], data2.y.shape[1]).double()
oneten2 = oneten2.to(device)
dstrain = data1.x[:, 10:]
dstrain_real = torch.mul(dstrain + coeff2, range3) / coeff1 + min3
# dstrainpl = input12 # predicted plastic strain increment
dstrainpl = target12 # actual plastic strain increment
dstrainpl_real = torch.mul(dstrainpl + coeff2, range1[1:4]) / coeff1 + min1[1:4]
dstrainel = dstrain_real - dstrainpl_real
dstrainelv = torch.matmul(dstrainel, oneten1)
dstrainelvten = torch.mul(dstrainelv, oneten2)
mu = torch.mul(param, input21[:, 0:1])
input22 = 2.0 * torch.mul(mu, dstrainel)
input23 = torch.mul((input21[:, 0:1] - (2.0 / 3.0) * mu), dstrainelvten)
input24 = input22 + input23
input2 = coeff1 * torch.div((input24 - min2), range2) - coeff2
target2 = data2.y
cost2 = criterion(input2, target2)
cost = cost11 + cost12 + cost2
return cost, cost11, cost12, cost2
def mean(inp):
output = (inp[0][0] + inp[1][0] + inp[2][0]) / 3.0
return output
def deviator(inp):
p = mean(inp)
s = np.array([[inp[0][0] - p], [inp[1][0] - p], [inp[2][0] - p], [inp[3][0]], [inp[4][0]], [inp[5][0]]])
return s
def j2(inp):
s = deviator(inp)
out = 0.0
for i in range(6):
out += s[i][0] ** 2.0 / 2.0
return out
def eq(inp):
j2_val = j2(inp)
out = np.sqrt(3.0 * j2_val)
return out