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Documentation for v0.1 version (#199)

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* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
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36
pina/operators.py
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@@ -1,4 +1,10 @@
"""Module for operators vectorize implementation"""
"""
Module for operators vectorize implementation. Differential operators are used to write any differential problem.
These operators are implemented to work on different accellerators: CPU, GPU, TPU or MPS.
All operators take as input a tensor onto which computing the operator, a tensor with respect
to which computing the operator, the name of the output variables to calculate the operator
for (in case of multidimensional functions), and the variables name on which the operator is calculated.
"""
import torch
from pina.label_tensor import LabelTensor
@@ -48,15 +54,15 @@ def grad(output_, input_, components=None, d=None):
raise RuntimeError('derivative labels missing from input tensor')
output_fieldname = output_.labels[0]
gradients = torch.autograd.grad(
output_,
input_,
grad_outputs=torch.ones(output_.size(), dtype=output_.dtype,
device=output_.device),
create_graph=True,
retain_graph=True,
allow_unused=True
)[0]
gradients = torch.autograd.grad(output_,
input_,
grad_outputs=torch.ones(
output_.size(),
dtype=output_.dtype,
device=output_.device),
create_graph=True,
retain_graph=True,
allow_unused=True)[0]
gradients.labels = input_.labels
gradients = gradients.extract(d)
@@ -188,11 +194,13 @@ def laplacian(output_, input_, components=None, d=None, method='std'):
result = torch.zeros(output_.shape[0], 1, device=output_.device)
for i, label in enumerate(grad_output.labels):
gg = grad(grad_output, input_, d=d, components=[label])
result[:, 0] += super(torch.Tensor, gg.T).__getitem__(i) # TODO improve
result[:, 0] += super(torch.Tensor,
gg.T).__getitem__(i) # TODO improve
labels = [f'dd{components[0]}']
else:
result = torch.empty(input_.shape[0], len(components),
result = torch.empty(input_.shape[0],
len(components),
device=output_.device)
labels = [None] * len(components)
for idx, (ci, di) in enumerate(zip(components, d)):
@@ -237,8 +245,8 @@ def advection(output_, input_, velocity_field, components=None, d=None):
if components is None:
components = output_.labels
tmp = grad(output_, input_, components, d
).reshape(-1, len(components), len(d)).transpose(0, 1)
tmp = grad(output_, input_, components, d).reshape(-1, len(components),
len(d)).transpose(0, 1)
tmp *= output_.extract(velocity_field)
return tmp.sum(dim=2).T