update docs

docs/source/index.rst

@@ -6,17 +6,40 @@
 f3dasm
 ======
 
+
 .. toctree::
-   :maxdepth: 1
+   :name: gettingstartedtoc
+   :caption: Getting started
+   :maxdepth: 3
    :hidden:
-
+   :includehidden:
+
    rst_doc_files/general/overview
    rst_doc_files/general/installation
-   rst_doc_files/defaults
+
+.. toctree::
+   :name: functionalitiestoc
+   :caption: Functionalities
+   :maxdepth: 3
+   :hidden:
+   :includehidden:
+
    auto_examples/index
+   rst_doc_files/defaults
+
+.. toctree::
+   :name: apitoc
+   :caption: API
+   :hidden:
+
+   rst_doc_files/reference/index.rst
    API reference <_autosummary/f3dasm>
 
 .. toctree::
+   :name: licensetoc
+   :caption: License
    :hidden:
 
+   license.rst
+
 .. include:: readme.rst

docs/source/license.rst

@@ -0,0 +1,33 @@
+.. _license:
+
+BSD 3-Clause License
+====================
+
+Copyright (c) 2022, Martin van der Schelling
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

docs/source/readme.rst

@@ -1,128 +1,60 @@
-Welcome to the documentation page of the 'Framework for Data-Driven Design and Analysis of Structures and Materials'.
-Here you will find all information on installing, using and contributing to the Python package.
+.. image:: ./img/f3dasm_logo_long.png
+    :align: center
+    :width: 70%
 
-Basic Concepts
---------------
+|
 
+Summary
+-------
 **f3dasm** introduces a general and user-friendly data-driven Python package for researchers and practitioners working on design and analysis of materials and structures.
-Some of the key features of are:
-
--  Modular design 
-
-    - The framework introduces flexible interfaces, allowing users to easily integrate their own models and algorithms.
-
-- Automatic data management
-
-    -  the framework automatically manages I/O processes, saving you time and effort implementing these common procedures.
-
-- :doc:`Easy parallelization <auto_examples/005_workflow/001_cluster_computing>`
-
-    - the framework manages parallelization of experiments, and is compatible with both local and high-performance cluster computing.
-
-- :doc:`Built-in defaults <rst_doc_files/defaults>`
-
-    - The framework includes a collection of :ref:`benchmark functions <implemented-benchmark-functions>`, :ref:`optimization algorithms <implemented optimizers>` and :ref:`sampling strategies <implemented samplers>` to get you started right away!
 
-- :doc:`Hydra integration <auto_examples/006_hydra/001_hydra_usage>`
 
-    - The framework is integrated with `hydra <https://hydra.cc/>`_ configuration manager, to easily manage and run experiments.
+.. image:: ./img/data-driven-process.png
+    :align: center
+    :width: 100%
 
-
-.. .. include:: auto_examples/001_domain/index.rst
-
-.. .. include:: auto_examples/002_experimentdata/index.rst
-
-.. .. include:: auto_examples/003_datageneration/index.rst
-
-.. .. include:: auto_examples/004_optimization/index.rst
+----
 
 Getting started
 ---------------
-
 The best way to get started is to:
 
 * Read the :ref:`overview` section, containing a brief introduction to the framework and a statement of need.
 * Follow the :ref:`installation-instructions` to get going!
 * Check out the :ref:`examples` section, containing a collection of examples to get you familiar with the framework.
 
+----
+
 Authorship & Citation
 ---------------------
-
 :mod:`f3dasm` is created and maintained by Martin van der Schelling [1]_.
 
 .. [1] PhD Candiate, Delft University of Technology, `Website <https://mpvanderschelling.github.io/>`_ , `GitHub <https://github.com/mpvanderschelling/>`_
 
-.. If you use :mod:`f3dasm` in your research or in a scientific publication, it is appreciated that you cite the paper below:
-
-.. **Computer Methods in Applied Mechanics and Engineering** (`paper <https://doi.org/10.1016/j.cma.2017.03.037>`_):
-
-.. .. code-block:: tex
-
-..     @article{Bessa2017,
-..     title={A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality},
-..     author={Bessa, Miguel A and Bostanabad, Ramin and Liu, Zeliang and Hu, Anqi and Apley, Daniel W and Brinson, Catherine and Chen, Wei and Liu, Wing Kam},
-..     journal={Computer Methods in Applied Mechanics and Engineering},
-..     volume={320},
-..     pages={633--667},
-..     year={2017},
-..     publisher={Elsevier}
-..     }
-
-
-.. Statement of Need
-.. -----------------
-
-.. The use of state-of-the-art machine learning tools for innovative structural and materials design has demonstrated their potential in various studies. 
-.. Although the specific applications may differ, the data-driven modelling and optimization process remains the same. 
-.. Therefore, the framework for data-driven design and analysis of structures and materials (:mod:`f3dasm`) is an attempt to develop a systematic approach of inverting the material design process. 
-
-
-.. The framework, originally proposed by Bessa et al. [3]_ integrates the following fields:
-
-.. - **Design \& Sampling**, in which input variables describing the microstructure, structure, properties and external conditions of the system to be evaluated are determined and sampled.
-.. - **Simulation**, typically through computational analysis, resulting in the creation of a material response database.
-.. - **Machine learning**, in which a surrogate model is trained to fit experimental findings.
-.. - **Optimization**, where we try to iteratively improve the model to obtain a superior design.
-
-.. The effectiveness of the first published version of :mod:`f3dasm` framework has been demonstrated in various computational mechanics and materials studies, 
-.. such as the design of a super-compressible meta-material [4]_ and a spiderweb nano-mechanical resonator inspired 
-.. by nature and guided by machine learning [5]_. 
-
-.. .. [3] Bessa, M. A., Bostanabad, R., Liu, Z., Hu, A., Apley, D. W., Brinson, C., Chen, W., & Liu, W. K. (2017). 
-..         *A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality. 
-..         Computer Methods in Applied Mechanics and Engineering*, 320, 633-667.
-
-.. .. [4] Bessa, M. A., Glowacki, P., & Houlder, M. (2019). 
-..         *Bayesian machine learning in metamaterial design: 
-..         Fragile becomes supercompressible*. Advanced Materials, 31(48), 1904845.
-
-.. .. [5] Shin, D., Cupertino, A., de Jong, M. H., Steeneken, P. G., Bessa, M. A., & Norte, R. A. (2022). 
-..         *Spiderweb nanomechanical resonators via bayesian optimization: inspired by nature and guided by machine learning*. Advanced Materials, 34(3), 2106248.
-
+----
 
 Contribute
 ----------
-
 :mod:`f3dasm` is an open-source project, and contributions of any kind are welcome and appreciated. If you want to contribute, please go to the `GitHub wiki page <https://github.com/bessagroup/f3dasm/wiki>`_.
 
+----
 
 Useful links
 ------------
-
 * `GitHub repository <https://github.com/bessagroup/F3DASM/tree/main>`_ (source code)
 * `Wiki for development <https://github.com/bessagroup/F3DASM/wiki>`_
 * `PyPI package <https://pypi.org/project/f3dasm/>`_ (distribution package)
 
 Related extension libraries
 ---------------------------
 * `f3dasm_optimize <https://github.com/bessagroup/f3dasm_optimize>`_: Optimization algorithms for the :mod:`f3dasm` package.
-.. * `f3dasm_simulate <https://github.com/bessagroup/f3dasm_optimize>`_: Simulators for the :mod:`f3dasm` package.
-.. * `f3dasm_teach <https://github.com/mpvanderschelling/f3dasm_teach>`_: Hub for practical session and educational material on using :mod:`f3dasm`.
+
+----
 
 License
 -------
 Copyright 2024, Martin van der Schelling
 
 All rights reserved.
 
-:mod:`f3dasm` is a free and open-source software published under a `BSD 3-Clause License <https://github.com/bessagroup/f3dasm/blob/main/LICENSE>`_.
+:mod:`f3dasm` is a free and open-source software published under a :doc:`BSD 3-Clause License <./license>`.