folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

version 0.0.1

Browse Source
This commit is contained in:
Your Name
2022-02-11 16:44:37 +01:00
parent fa8ffd5042
commit 1483746b45
29 changed files with 416 additions and 559 deletions
Download Patch File Download Diff File Expand all files Collapse all files

31
pina/pinn.py
View File

@@ -163,17 +163,16 @@ class PINN(object):
if locations == 'all':
locations = [condition for condition in self.problem.conditions]
for location in locations:
manifold, func = self.problem.conditions[location].values()
if torch.is_tensor(manifold):
pts = manifold
else:
pts = manifold.discretize(n, mode)
condition = self.problem.conditions[location]
pts = torch.from_numpy(pts)
try:
pts = condition.location.sample(n, mode)
except:
pts = condition.input_points
self.input_pts[location] = LabelTensor(pts, self.problem.input_variables)
self.input_pts[location] = pts
print(pts.tensor.shape)
self.input_pts[location].tensor.to(dtype=self.dtype, device=self.device)
self.input_pts[location].tensor.requires_grad_(True)
self.input_pts[location].tensor.retain_grad()
@@ -203,17 +202,15 @@ class PINN(object):
while True:
losses = []
for condition_name in self.problem.conditions:
condition = self.problem.conditions[condition_name]
pts = self.input_pts[condition_name]
predicted = self.model(pts.tensor)
if isinstance(self.problem.conditions[condition_name]['func'], list):
for func in self.problem.conditions[condition_name]['func']:
residuals = func(pts, predicted)
losses.append(self._compute_norm(residuals))
else:
residuals = self.problem.conditions[condition_name]['func'](pts, predicted)
losses.append(self._compute_norm(residuals))
#print(condition_name, losses[-1])
predicted = self.model(pts)
residuals = condition.function(pts, predicted)
losses.append(self._compute_norm(residuals))
self.optimizer.zero_grad()
sum(losses).backward()