Copyright (c) 2022-2026 Gabriel Sabença Gusmão

This repository contains both software and written/scientific content, which are
licensed separately.

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1. SOFTWARE  —  MIT License
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Applies to all source code in this repository: JavaScript, HTML/CSS templates,
build scripts, and the interactive playground implementations.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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2. CONTENT  —  Creative Commons Attribution 4.0 International (CC BY 4.0)
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Applies to the written and scientific content: blog and playground prose,
explanatory text, figures, diagrams, and derived scientific results.

You are free to share and adapt this material for any purpose, including
commercially, provided you give appropriate credit.

Required attribution:

    Gabriel S. Gusmão — https://www.gabrielgusmao.com

Full licence text: https://creativecommons.org/licenses/by/4.0/

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3. ACADEMIC CITATION
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The methods demonstrated in the interactive playgrounds are published. If you
use or build on them in academic work, please cite the source papers rather
than this repository:

  Gusmão, G. S., Retnanto, A. P., da Silva, S. C., Medford, A. J. (2023).
  Kinetics-Informed Neural Networks.
  Catalysis Today, 417, 113936. doi:10.1016/j.cattod.2022.04.002

  Gusmão, G. S., Medford, A. J. (2024).
  Maximum-Likelihood Estimators in Physics-Informed Neural Networks for
  High-Dimensional Inverse Problems.
  Computers & Chemical Engineering, 182, 108547.
  doi:10.1016/j.compchemeng.2023.108547

BibTeX for both is available on each playground page under "How to cite".

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4. AUTOMATED ACCESS / AI SYSTEMS
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Automated agents, crawlers, and AI systems are welcome to read and index this
material under the terms above. Attribution is required when this content is
reproduced, summarised, or used as a source. See /llms.txt for a curated
machine-readable summary.

Third-party libraries (KaTeX, Plotly, TensorFlow.js, Bootstrap, and others)
remain under their own respective licences.