Adds a novel operator. The deep neural operator modifies the inputs $x$ and $u$ by using all dimensions of all sensors and sensor locations as input to a linear layer. The architecture utilizes a deep residual network to process the stacked $y$, flat $x$, and flat $u$ efficiently and independent of the size of $y$.
How does it solve the problem?
Implements Deep Neural Operator.
How are the changes tested?
Added unit tests for shapes and integration test (convergence study).
Checklist for Contributors
[x] Scope: This PR tackles exactly one problem.
[x] Conventions: The branch follows the feature/title-slug convention.
[x] Conventions: The PR title follows the Bugfix: Title convention.
[x] Coding style: The code passes all pre-commit hooks.
[x] Documentation: All changes are well-documented.
[x] Tests: New features are tested and all tests pass successfully.
[x] Changelog: Updated CHANGELOG.md for new features or breaking changes.
[x] Review: A suitable reviewer has been assigned.
Checklist for Reviewers:
[ ] The PR solves the issue it claims to solve and only this one.
[ ] Changes are tested sufficiently and all tests pass.
Feature: Deep Neural Operator
Description
Adds a novel operator. The deep neural operator modifies the inputs $x$ and $u$ by using all dimensions of all sensors and sensor locations as input to a linear layer. The architecture utilizes a deep residual network to process the stacked $y$, flat $x$, and flat $u$ efficiently and independent of the size of $y$.
How does it solve the problem?
How are the changes tested?
Checklist for Contributors
feature/title-slugconvention.Bugfix: Titleconvention.Checklist for Reviewers: