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Add example page.
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samuelburbulla committed Jan 23, 2024
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28 changes: 28 additions & 0 deletions docs/examples/index.md
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---
title: Examples
---

This is a collection of notebooks that showcase various applications of
Continuity.

::cards:: cols=2

- title: The Basics
content: Learning function operators with Continuity
url: basics

- title: Physics-informed
content: >
Training physics-informed neural operators
url: physicsinformed

- title: Self-supervised
content: >
Self-supervised training of operators
url: selfsupervised

- title: Super-resolution
content: Neural operators for super-resolution
url: superresolution

::/cards::
9 changes: 7 additions & 2 deletions docs/index.md
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Expand Up @@ -26,11 +26,16 @@ examples and benchmarks.

- title: Learning Operators
content: >
Basics of learning function operators with neural networks
Basics of learning function operators
url: operators/index.md

- title: Examples
content: >
Some notebooks using Continuity
url: examples/index.md

- title: Browse the API
content: Full documentation of the API
content: Full class documentation
url: api/continuity/index.md

::/cards::
2 changes: 1 addition & 1 deletion docs/operators/index.md
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Expand Up @@ -86,4 +86,4 @@ Neural operators extend the concept of neural networks to function mappings, whi
enables discretization-invariant and mesh-free mappings of data with
applications to physics-informed training, super-resolution, and more.

See our examples (e.g., <a href="../examples/basics">Basics</a>) for more details and further reading.
See our <a href="../examples">Examples</a> for more details and further reading.
2 changes: 2 additions & 0 deletions src/continuity/__init__.py
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"""
**Continuity** is a Python package for machine learning on function operators.
The package is structured into the following modules:
::cards:: cols=2
- title: Operators
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6 changes: 4 additions & 2 deletions src/continuity/data/__init__.py
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"""
This defines DataSets in Continuity.
Every data set is a list of (x, u, y, v) tuples.
`continuity.data`
Data sets in Continuity.
Every data set is a list of `(x, u, y, v)` tuples.
"""

import math
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13 changes: 12 additions & 1 deletion src/continuity/operators/__init__.py
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"""Operators in Continuity."""
"""
`continuity.operators`
Operators in Continuity.
Every operator maps collocation points `x`, function values `u`,
and evaluation points `y` to evaluations of `v`:
```
v = operator(x, u, y)
```
"""

from .operator import Operator
from .deeponet import DeepONet
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8 changes: 7 additions & 1 deletion src/continuity/pde/__init__.py
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"""Loss functions for physics-informed training."""
"""
`continuity.pde`
PDEs in Continuity.
Every PDE is implemented using a physics-informed loss function.
"""

from torch import Tensor
from abc import abstractmethod
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6 changes: 5 additions & 1 deletion src/continuity/plotting/__init__.py
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"""Plotting utilities for Continuity."""
"""
`continuity.plotting`
Plotting utilities for Continuity.
"""

import torch
import numpy as np
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