# 🔬 Scientific Background & Acknowledgments

[`⬅️ README`](README.md) | [`📚 THEORY`](THEORY.md) | [`👥 AUTHORS`](AUTHORS.md)

This project came to life by implementing cutting-edge research in white-box machine learning. We would like to deeply acknowledge the following colleagues for the works, research teams, and foundational authors whose theoretical breakthroughs made the TAM framework possible:

## 📐 Foundational Theory & Inspiration (GAMs)

  * **Simon N. Wood:** We acknowledge his definitive foundational work on Generalized Additive Models (GAMs). His research mathematically underpins continuous structural regularization, Penalized Splines, and scale-invariant tensor products.

## 🏗️ Core WeaKL Framework (StaticTAM, AdaptiveTAM, HierarchicalTAM)

  * **Nathan Doumèche, Francis Bach, Éloi Bedek, Gérard Biau, Claire Boyer, and Yannig Goude:** Their 2025 research on forecasting time series with constraints serves as the theoretical foundation for the WeaKL algorithm.
  * **Nathan Doumèche:** His 2025 PhD thesis provides the central reference for the "Online WeaKL" approach, utilized as a tensorial alternative for managing structural breaks.
  * **Nathan Doumèche, Francis Bach, Gérard Biau, and Claire Boyer:** Their work on Physics-Informed Kernel Learning (PIKL) demonstrates that linear differential constraints can be reduced to a convex quadratic penalty, forming the basis of our exact physics module.

## ⚖️ Expert Aggregation (OperaTAM)

  * **Pierre Gaillard and Yannig Goude:** We acknowledge their foundational 2016 `OPERA` package. Their theoretical frameworks for the MLpol and EWA algorithms are implemented natively for the OperaTAM module.

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## 🌐 Extended Mathematical & Algorithmic Influences

This framework is also built upon the mathematical foundations laid by the researchers cited in the references.bib file.