Dimensional Substrate Structures#

References#

This appendix lists references relevant to dimensional‑substrate theory, high‑dimensional modeling, scaling behavior, regime analysis, and validation frameworks. Citations are grouped by category for clarity and presented in a substrate‑agnostic, model‑independent format consistent with the RSM canon.

1. Dimensional Modeling and High‑Dimensional Geometry#

• Baraniuk, R.
  Compressive Sensing.
  IEEE Signal Processing Magazine 24, 118–121 (2007).

• Bengio, Y., Courville, A., & Vincent, P.
  Representation Learning: A Review and New Perspectives.
  IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1798–1828 (2013).

• Coifman, R. R., & Lafon, S.
  Diffusion Maps.
  Applied and Computational Harmonic Analysis 21, 5–30 (2006).

• Tenenbaum, J. B., de Silva, V., & Langford, J. C.
  A Global Geometric Framework for Nonlinear Dimensionality Reduction.
  Science 290, 2319–2323 (2000).

2. Scaling Laws and High‑Dimensional Systems#

• Kaplan, J., McCandlish, S., Henighan, T., et al.
  Scaling Laws for Neural Language Models.
  arXiv:2001.08361 (2020).

• Bahri, Y., Kadmon, J., Pennington, J., et al.
  Statistical Mechanics of Deep Learning.
  Annual Review of Condensed Matter Physics 11, 501–528 (2020).

• Lin, H. W., Tegmark, M., & Rolnick, D.
  Why Does Deep and Cheap Learning Work So Well?
  Journal of Statistical Physics 168, 1223–1247 (2017).

3. Regime Behavior and Stability Analysis#

• Strogatz, S.
  Nonlinear Dynamics and Chaos.
  Westview Press (2014).

• Ott, E.
  Chaos in Dynamical Systems.
  Cambridge University Press (2002).

• Guckenheimer, J., & Holmes, P.
  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields.
  Springer (1983).

4. Validation, Invariants, and Drift Detection#

• Breck, E., Cai, S., Nielsen, E., et al.
  The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction.
  Google Research (2017).

• Sculley, D., Holt, G., Golovin, D., et al.
  Hidden Technical Debt in Machine Learning Systems.
  NIPS (2015).

• Amershi, S., Begel, A., Bird, C., et al.
  Software Engineering for Machine Learning: A Case Study.
  ICSE‑SEIP (2019).

5. Substrate‑Level and Triadic‑Frameworks Canon#

• Loswin, N.
  Resonance Substrate Model (RSM): Structural Foundations for High‑Dimensional Inference.
  TriadicFrameworks (2025).

• Loswin, N.
  Triadic Dimensional Cores: A 3D–9D Substrate for Structural and Inference‑Level Alignment.
  TriadicFrameworks (2025).

• Loswin, N.
  Validation‑Space‑Time (vST): A Substrate‑Level Framework for Reproducibility and Drift Detection.
  TriadicFrameworks (2025).

• Loswin, N.
  Dimensional Substrate Structures: Scaling Laws and High‑Dimensional Regimes.
  TriadicFrameworks (2026).

6. Additional Resources#

• Cover, T. M., & Thomas, J. A.
  Elements of Information Theory.
  Wiley (2006).

• Bishop, C. M.
  Pattern Recognition and Machine Learning.
  Springer (2006).

• Goodfellow, I., Bengio, Y., & Courville, A.
  Deep Learning.
  MIT Press (2016).