:orphan:
.. _examples_gallery:
Examples
========
Fifteen complete, runnable studies, each built around a realistic
scenario from a different domain: materials processing, CFD, wet-lab
biology, machine-learning hyperparameters, particle phenomenology and
beamline optics, among others. Every example is executed on every
documentation build, so each page shows the full script together with
the exact output and figures that script produces, plus download links
for the ``.py`` source and a generated Jupyter notebook.
To run any example from a clone of the repository::
python examples/01_quickstart.py
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.. image:: /auto_examples/images/thumb/sphx_glr_01_quickstart_thumb.png
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:doc:`/auto_examples/01_quickstart`
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Quickstart: a first design
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.. image:: /auto_examples/images/thumb/sphx_glr_02_parameter_types_thumb.png
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:doc:`/auto_examples/02_parameter_types`
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Mixed parameter types and a constraint
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.. image:: /auto_examples/images/thumb/sphx_glr_03_advanced_design_thumb.png
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:doc:`/auto_examples/03_advanced_design`
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Prescribed points, focus and exclusion
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.. image:: /auto_examples/images/thumb/sphx_glr_04_choosing_criteria_thumb.png
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:doc:`/auto_examples/04_choosing_criteria`
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Comparing optimisation criteria
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.. image:: /auto_examples/images/thumb/sphx_glr_05_choosing_algorithm_thumb.png
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:doc:`/auto_examples/05_choosing_algorithm`
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Choosing an optimisation algorithm
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.. image:: /auto_examples/images/thumb/sphx_glr_06_sample_size_thumb.png
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:doc:`/auto_examples/06_sample_size`
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How large should a design be?
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.. image:: /auto_examples/images/thumb/sphx_glr_07_extra_sets_thumb.png
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:doc:`/auto_examples/07_extra_sets`
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Extra sets: adding a test set
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.. image:: /auto_examples/images/thumb/sphx_glr_08_resume_existing_thumb.png
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:doc:`/auto_examples/08_resume_existing`
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Resuming from an existing design
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.. image:: /auto_examples/images/thumb/sphx_glr_09_sequential_workflow_thumb.png
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:doc:`/auto_examples/09_sequential_workflow`
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A staged, sequential campaign
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.. image:: /auto_examples/images/thumb/sphx_glr_10_outputs_and_reports_thumb.png
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:doc:`/auto_examples/10_outputs_and_reports`
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Outputs and reports in one place
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.. image:: /auto_examples/images/thumb/sphx_glr_11_wetlab_biology_thumb.png
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:doc:`/auto_examples/11_wetlab_biology`
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Wet-lab: an assay with a nominal factor
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.. image:: /auto_examples/images/thumb/sphx_glr_12_cfd_engineering_thumb.png
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:doc:`/auto_examples/12_cfd_engineering`
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CFD: a sweep with a turbulence model
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.. image:: /auto_examples/images/thumb/sphx_glr_13_ml_hyperparameters_thumb.png
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:doc:`/auto_examples/13_ml_hyperparameters`
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Machine-learning hyperparameters
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.. image:: /auto_examples/images/thumb/sphx_glr_14_bsm_2hdm_phenomenology_thumb.png
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:doc:`/auto_examples/14_bsm_2hdm_phenomenology`
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Particle physics: 2HDM benchmarks
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.. image:: /auto_examples/images/thumb/sphx_glr_15_beamline_optics_thumb.png
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:doc:`/auto_examples/15_beamline_optics`
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Beamline optics: costly settings
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.. toctree::
:hidden:
/auto_examples/01_quickstart
/auto_examples/02_parameter_types
/auto_examples/03_advanced_design
/auto_examples/04_choosing_criteria
/auto_examples/05_choosing_algorithm
/auto_examples/06_sample_size
/auto_examples/07_extra_sets
/auto_examples/08_resume_existing
/auto_examples/09_sequential_workflow
/auto_examples/10_outputs_and_reports
/auto_examples/11_wetlab_biology
/auto_examples/12_cfd_engineering
/auto_examples/13_ml_hyperparameters
/auto_examples/14_bsm_2hdm_phenomenology
/auto_examples/15_beamline_optics
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.. container:: sphx-glr-footer sphx-glr-footer-gallery
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download all examples in Python source code: auto_examples_python.zip `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip `
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.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery