H² Research Framework

A single mathematical spine — the Shannon → Von Neumann → Riemannian entropy hierarchy — producing validated predictions across five maximally distant domains. When it works on materials AND pure mathematics, the "overfitting" dismissal fails.

The Entropy Hierarchy

Every H² result traces to a single insight: entropy is not one thing — it's a hierarchy. Each level embeds the previous, and each embedding adds predictive power.

Level 0: Clausius entropy (1865) — thermodynamic, S = ∫dQ/T
embeds into
Level 1: Shannon entropy (1948) — information, H = −Σp log p
embeds into
Level 2: Von Neumann entropy (1932) — quantum, S = −Tr(ρ log ρ)
connected by Koopman-von Neumann bridge
Level 3: Riemannian geometry — curvature, holonomy, geodesic structure
unified by
MDL: Minimum Description Length — Nature's parsimony principle

This is not metaphor. Von Neumann named Shannon's quantity "entropy." The Koopman-von Neumann formalism (1931) lifts classical dynamics into Hilbert space. MDL (Rissanen 1978) selects the shortest description. The hierarchy is real, testable, and computationally exploitable.

Validated Results

Materials Science (H2m)

91% accuracy · 86,000× speedup
Phase transition classification on 12,525 material pairs (2,936 paired transitions). Zero-parameter formula achieves 9.7% MAPE on phonon prediction. 4 of 5 hypotheses validated. Golden Ratio universality honestly downgraded to MIXED (only 11.1% of pairs near φ).

Medical Monitoring (H2d-Med)

90% false alarm reduction · 100% sensitivity
Dwell-controlled state machines for rare physiological event detection. Patient-specific thresholds beat population averages. 3 of 5 hypotheses validated.

Dynamical Systems (KvN Channel Asymmetry)

AUC = 0.935 · Cross-domain AUC = 1.000
Koopman channel asymmetry δ discriminates near-transition from stationary dynamics at 3.31× ratio (p = 5.3 × 10−17). Spectral gap mechanism: λ⊂2;-δ Spearman ρ = −0.749. Frozen pipeline transfers to CWRU bearing faults with zero retraining.

Climate Tipping Points (H2Clime)

AMOC complexity-drop validated
AMOC/CESM slices show higher complexity-drop rates than empirical/paleoclimate under matched contracts. One general hypothesis (V7 holonomy improves detection) was falsified and openly retracted. Honest accounting is a feature, not a bug.

Pure Mathematics (Erdős MDL Mapper)

90-year conjecture · 1,179 problems mapped
The Cramér bound O((log N)²) for maximal prime gaps emerges from MDL parsimony alone — no Riemann Hypothesis needed. Validated on 108 primes (CV = 0.006), beats all 5 classical models (Hardy-Littlewood, Gallagher, Granville, PNT, geometric). Erdős Problem #233 product set bound proved in Lean 4 (all 6 lemmas machine-proved by Aristotle (Harmonic AI) — zero sorry stubs). 1,109 MDL morphisms across 68.4% of 1,179 Erdős problems — no prior art exists for this mapping.

The 1,179 Problem Project

Paul Erdős (1913–1996) posed 1,179 open problems across combinatorics, number theory, and discrete geometry. We're systematically translating them into MDL — the first such mapping ever attempted.

1,179
Problems Catalogued
1,109
MDL Morphisms
68.4%
Coverage
2
Problems Solved
14
Experiments
$34,670
Open Prize Pool

Papers & Preprints

Koopman Channel Asymmetry as an Early Warning Signal for Critical Transitions

Manuscript Complete

K. Mendoza. Target: Physical Review E / Chaos. Introduces δ = |Ffwd − Fbwd| as a novel operator-theoretic EWS diagnostic. AUC = 0.935 with MI-k10 feature selection.

Product Set Bounds via Minimum Description Length: An Information-Theoretic Approach to Erdős Problem #233

In Preparation

K. Mendoza. Target: arXiv math.CO. All 6 lemmas machine-proved in Lean 4 by Aristotle (Harmonic AI). Cramér bound emerges from MDL without RH assumption.

The Cramér-MDL Conjecture: Prime Gap Bounds from Information-Theoretic Parsimony

Data Complete

K. Mendoza. Target: arXiv math.NT. Empirical validation on 108 primes. CV = 0.006, 3.7× improvement over Granville refinement.

One Problem, Five Domains, Same Solution

Domain The Challenge H² Solution Result
Immune System Prevent premature activation on noise Multi-threshold hysteresis 90% false alarm reduction
Quantum Computing Prevent false syndrome detection Dwell-time hysteresis filter 895% improvement (hardware)
Materials Science Predict phase transitions without simulation Information-theoretic classification 91% accuracy, 86,000× speedup
Climate Detection Distinguish tipping points from noise Entropy-gradient thresholding AMOC complexity-drop validated
Dynamical Systems Early warning for critical transitions Koopman channel asymmetry δ AUC = 0.935; cross-domain 1.000
Pure Mathematics Maximal prime gap distribution MDL parsimony (no Riemann Hypothesis) CV = 0.006 on 10&sup8; primes
AI Systems Prevent optimization oscillation Entropy clamping + stability control Active (experiments in progress)

"This cross-domain insight wouldn't have emerged from staying within traditional disciplinary boundaries. Sometimes the best solution to a physics problem comes from biology — and the best validation of both comes from pure mathematics."

Patent-Backed Innovation

Each domain application is protected by patent filings. The core mathematical framework is held as trade secret; domain-specific implementations are patent-backed.

Quantum Error Correction

Hardware-validated on IBM Quantum. Multi-threshold dwell-time hysteresis reduces false syndrome triggers.

Materials & Battery Safety

107–108× speedup vs DFT simulation. Early warning for thermal runaway and dendrite formation.

AI & Music

Hallucination reduction via entropy-guided optimization. Music analysis using thermodynamic feature extraction.

Climate & Geoscience

Tipping point detection via entropy gradients. Geophysical inversion for subsurface imaging.

Immune & Mental Health

Danger Theory–inspired immune state estimation. Psychoneuroimmunology for mental health early warning.

Cybersecurity

Biomimetic apoptosis defense. Deepfake detection via entropy inversion signatures.

Explore Full Patent Portfolio →

Methodology: Honest Accounting

Every result in this portfolio follows strict scientific integrity rules:

Collaborate

Seeking collaborators in applied category theory, information geometry, formal mathematics, and cross-domain validation. Open to academic partnerships, licensing discussions, and adversarial review.

[email protected]