DOI: 10.5281/zenodo.18381205
title: Brahim's Laws for Wormhole Traversability

description:  



Brahim Wormhole Engine

The Brahim Wormhole Engine is a mathematical framework that implements Morris-Thorne traversable wormhole physics through the application of the corrected symmetric Brahim Sequence.

Core Innovation: Sequence Correction

The engine's primary breakthrough is the correction of sequence asymmetry to achieve full algebraic closure. By adjusting the original sequence, the engine ensures that all symmetrical pairs sum to a constant value of 214.





Original (Broken): $\{27, 42, 60, 75, 97, 121, 136, 154, 172, 187\}$




Corrected (Closed): $\{27, 42, 60, 75, 97, 117, 139, 154, 172, 187\}$




Property: $a[i] + a[9-i] = 214$



Technical Applications

The engine's mathematical properties allow for diverse applications across multiple fields:




#
Application
How It Works




1



Network Routing



Transform is a contraction mapping; all routes converge to the centroid at a rate of $1/\phi$.





2



Data Compression



Lossless hierarchical compression achieved through an iteration reduction of $\beta = 23.6\%$.





3



Error Detection



Mirror symmetry allows for instant corruption detection in sequences.





4



Cryptographic Hashing



Utilizes a fixed-point attractor at $r=0.809$ for collision-resistant hashes.





5



Machine Learning



Spectral abscissa $\gamma=0.146$ defines the maximum stable learning rate.





6



Signal Processing



Uses a $\phi$ hierarchy for maximally aperiodic filter banks, eliminating aliasing.





7



Financial Modeling



Negative eigenvalues provide a mathematical guarantee for mean reversion.





8



Physics Simulation



Implements full Morris-Thorne geometry to simulate wormhole throat dynamics.





Key Mathematical Results

The following results demonstrate the engine's stability and geometric precision:





Flare-out Condition: $b'(r_0) = -1/\phi = -0.618$ (Ensures proper wormhole geometry).




NEC Factor: $+\phi = +1.618$ (Calculates required exotic matter).




Eigenvalues: $\{-0.146, -0.618\}$ (Confirms asymptotic stability).




The Brahim Identity: $\alpha + \beta = 1/\phi$ (Exact within error $< 10^{-15}$).




Compression Constant: $\beta = 0.236$ (23.6% reduction per iteration).



The Discoveries

Recent advancements in the engine's theoretical framework have yielded significant breakthroughs in dimensional unification.

1. The Convergence Theorem

Dimensional constants converge at Least Common Multiple (LCM) dimensions according to the identity:

 
$$(1/\phi^{d_1})^{n/d_1} = (1/\phi^{d_2})^{n/d_2} = 1/\phi^n$$

Where $n = \text{LCM}(d_1, d_2)$.

2. The Unification Number

The strength of convergence is defined by the Unification Number $U(n)$, which is equivalent to the number of divisors of the dimension:

 
$$U(n) = |D(n)|$$

3. The Grand Unification Constant

The constant $\Phi_{12}$ represents the point where 2D, 3D, and 4D systems reach structural equilibrium:

 
$$\Phi_{12} = 1/\phi^{12} \approx 0.31\%$$

At this point: $\alpha^6 = \beta^4 = \gamma^3 = 1/\phi^{12}$.

4. The Grand Unification Sequence




Dim
Paths
Meaning




12



6



2D, 3D, and 4D systems unify.





24



8



Second-order unification threshold.





60



12



Complete convergence: 2D, 3D, 4D, 5D, and 6D all meet.





5. The 12-Fold Symmetry

Unification is governed by the geometry of $\phi$:





12 faces of a dodecahedron.




12 vertices of an icosahedron.




12 = LCM(2, 3, 4): The first point of complete dimensional unification.



The Formula of Everything

The updated model now incorporates:





Grand Unification: $\beta^4 = \gamma^3 = 1/\phi^{12} = 0.31\%$




Convergence Strength: $U(n) = |D(n)|$



Quick Usage Example

from src.core.brahim_wormhole_engine import BrahimWormholeEngine

# Initialize the engine
engine = BrahimWormholeEngine()

# 1. Network Routing
path = engine.route(source_vector, max_hops=10)

# 2. Hierarchical Compression
compressed = engine.compress(data, levels=5)

# 3. Error Detection
errors = engine.detect_errors(sequence)

# 4. Stability Analysis for ML
stability = engine.analyze_stability()
max_learning_rate = abs(stability.spectral_abscissa)  # Returns 0.146