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ede74c0 | 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 | # implementation of PINNsformer
# paper: PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
# link: https://arxiv.org/abs/2307.11833
import torch
import torch.nn as nn
from onescience.utils.pinnsformer_util import get_clones
class WaveAct(nn.Module):
def __init__(self):
super(WaveAct, self).__init__()
self.w1 = nn.Parameter(torch.ones(1), requires_grad=True)
self.w2 = nn.Parameter(torch.ones(1), requires_grad=True)
def forward(self, x):
return self.w1 * torch.sin(x)+ self.w2 * torch.cos(x)
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=256):
super(FeedForward, self).__init__()
self.linear = nn.Sequential(*[
nn.Linear(d_model, d_ff),
WaveAct(),
nn.Linear(d_ff, d_ff),
WaveAct(),
nn.Linear(d_ff, d_model)
])
def forward(self, x):
return self.linear(x)
class EncoderLayer(nn.Module):
def __init__(self, d_model, heads):
super(EncoderLayer, self).__init__()
self.attn = nn.MultiheadAttention(embed_dim=d_model, num_heads=heads, batch_first=True)
self.ff = FeedForward(d_model)
self.act1 = WaveAct()
self.act2 = WaveAct()
def forward(self, x):
x2 = self.act1(x)
# pdb.set_trace()
x = x + self.attn(x2,x2,x2)[0]
x2 = self.act2(x)
x = x + self.ff(x2)
return x
class DecoderLayer(nn.Module):
def __init__(self, d_model, heads):
super(DecoderLayer, self).__init__()
self.attn = nn.MultiheadAttention(embed_dim=d_model, num_heads=heads, batch_first=True)
self.ff = FeedForward(d_model)
self.act1 = WaveAct()
self.act2 = WaveAct()
def forward(self, x, e_outputs):
x2 = self.act1(x)
x = x + self.attn(x2, e_outputs, e_outputs)[0]
x2 = self.act2(x)
x = x + self.ff(x2)
return x
class Encoder(nn.Module):
def __init__(self, d_model, N, heads):
super(Encoder, self).__init__()
self.N = N
self.layers = get_clones(EncoderLayer(d_model, heads), N)
self.act = WaveAct()
def forward(self, x):
for i in range(self.N):
x = self.layers[i](x)
return self.act(x)
class Decoder(nn.Module):
def __init__(self, d_model, N, heads):
super(Decoder, self).__init__()
self.N = N
self.layers = get_clones(DecoderLayer(d_model, heads), N)
self.act = WaveAct()
def forward(self, x, e_outputs):
for i in range(self.N):
x = self.layers[i](x, e_outputs)
return self.act(x)
class PINNsformer1D(nn.Module):
def __init__(self, d_out, d_model, d_hidden, N, heads):
super(PINNsformer1D, self).__init__()
self.linear_emb = nn.Linear(2, d_model)
self.encoder = Encoder(d_model, N, heads)
self.decoder = Decoder(d_model, N, heads)
self.linear_out = nn.Sequential(*[
nn.Linear(d_model, d_hidden),
WaveAct(),
nn.Linear(d_hidden, d_hidden),
WaveAct(),
nn.Linear(d_hidden, d_out)
])
def forward(self, x, t):
src = torch.cat((x,t), dim=-1)
src = self.linear_emb(src)
e_outputs = self.encoder(src)
d_output = self.decoder(src, e_outputs)
output = self.linear_out(d_output)
# pdb.set_trace()
# raise Exception('stop')
return output
class PINNsformer2D(nn.Module):
def __init__(self, d_out, d_model, d_hidden, N, heads):
super(PINNsformer2D, self).__init__()
self.linear_emb = nn.Linear(3, d_model)
self.encoder = Encoder(d_model, N, heads)
self.decoder = Decoder(d_model, N, heads)
self.linear_out = nn.Sequential(*[
nn.Linear(d_model, d_hidden),
WaveAct(),
nn.Linear(d_hidden, d_hidden),
WaveAct(),
nn.Linear(d_hidden, d_out)
])
def forward(self, x, y, t):
src = torch.cat((x,y,t), dim=-1)
src = self.linear_emb(src)
e_outputs = self.encoder(src)
d_output = self.decoder(src, e_outputs)
output = self.linear_out(d_output)
return output
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