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36 changes: 22 additions & 14 deletions README.md
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# PEtab SciML
*A data format for scientific machine learning*
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<source media="(prefers-color-scheme: dark)" srcset="doc/logo/logo_dark.png">
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_Data format and tooling for scientific machine learning modeling_

[![Build Status](https://github.com/PEtab-dev/petab_sciml/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/PEtab-dev/petab_sciml/actions/workflows/ci.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/PEtab-dev/petab_sciml/graph/badge.svg?token=ki1YcdIHII)](https://codecov.io/gh/PEtab-dev/petab_sciml)

[Getting Started](https://petab-sciml.readthedocs.io/latest/examples/getting_started/getting_started.html) |
[Documentation](https://petab-sciml.readthedocs.io/latest/introduction.html) |
[Getting Started](https://petab-sciml.readthedocs.io/latest/examples/getting_started/getting_started.html)
| [Documentation](https://petab-sciml.readthedocs.io/latest/introduction.html) |
[Contributing](https://petab-sciml.readthedocs.io/latest/_tmp/CONTRIBUTING.html)

PEtab SciML is a table-based data format for creating training (parameter estimation)
problems for **scientific machine learning (SciML)** models that combine machine learning
and mechanistic ordinary differential equation (ODE) models.

> [!WARNING]
> **Beta Disclaimer**: this software is under active development and may contain bugs or instabilities. The PEtab SciML format is finalised and support for it has been implemented in PEtab importers, though not yet released. Documentation and utility functions are currently being added.
> [!WARNING] **Beta Disclaimer**: this software is under active development and may contain
> bugs or instabilities. The PEtab SciML format is finalised and support for it has been
> implemented in PEtab importers, though not yet released. Documentation and utility
> functions are currently being added.

## Major features

Extending the [PEtab format](https://petab.readthedocs.io) for mechanistic ODE models,
PEtab SciML provides a human readable, reproducible way to specify SciML training problems
across diverse scenarios, in a format directly importable by downstream tools. The main
aspects enabling this are:
Extending the [PEtab format](https://petab.readthedocs.io) for mechanistic ODE models, PEtab
SciML provides a human readable, reproducible way to specify SciML training problems across
diverse scenarios, in a format directly importable by downstream tools. The main aspects
enabling this are:

- **Flexible hybridization.** Machine learning (ML) and ODE models can be combined in three
ways: (1) ML within the ODE dynamics (includes **Neural ODEs**), (2) ML in the
Expand All @@ -30,10 +39,9 @@ aspects enabling this are:
toolboxes for dynamic-model training in Julia
([PEtab.jl](https://github.com/sebapersson/PEtab.jl)) and Python/JAX
([AMICI](https://github.com/AMICI-dev/AMICI)).
- **Broad support for ML architectures.** A diverse set of ML architectures can be
specified via an exchangeable PEtab SciML YAML format (supports export from PyTorch
modules), or via importer-specific libraries (e.g., Lux.jl in PEtab.jl; Equinox in
AMICI).
- **Broad support for ML architectures.** A diverse set of ML architectures can be specified
via an exchangeable PEtab SciML YAML format (supports export from PyTorch modules), or via
importer-specific libraries (e.g., Lux.jl in PEtab.jl; Equinox in AMICI).
- **Diverse model types.** All model features of the
[PEtab format](https://petab.readthedocs.io) are supported, like models with partial
observability, multiple simulation conditions, diverse noise models, and/or events.
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"type": "fontawesome",
},
],
"logo": {
"image_light": "logo/logo_light.png",
"image_dark": "logo/logo_dark.png",
"alt_text": "PEtab SciML",
},
"navbar_center": ["navbar-nav"],
"navbar_end": ["theme-switcher", "navbar-icon-links"],
"navbar_persistent": ["search-button"],
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Format Specification <format>
Supported Layers and Activation Functions <layers>
Development process <development>
PEtab SciML logo license <logo>
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PEtab SciML logo license
========================

The PEtab SciML logo is free to use under the
`CC0 <https://creativecommons.org/share-your-work/public-domain/cc0>`__ license
and is available in both SVG and PNG formats in the
`logo directory <https://github.com/PEtab-dev/petab_sciml/tree/main/doc/logo>`__.
Feel free to use it in any presentation or publication that uses PEtab SciML.

.. container:: figure align-center

.. image:: logo/logo_light.png
:class: only-light

.. image:: logo/logo_dark.png
:class: only-dark

.. container:: caption
Binary file added doc/logo/logo_dark.png
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