The Informational Gradient Energy Decay Law states that when an informational field evolves under Laplacian smoothing, the total gradient energy strictly decreases over time.
Gradients weaken. Sharp changes fade. Energy decays monotonically.
This law is a foundational property of diffusion processes, PDE smoothing, and informational relaxation systems.
Based on the uploaded file:
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Gradient operator: G = roll(A,-1) - roll(A,1) Energy: E = Σ (G²) Evolution: A ← A - α * G Result: E(t) strictly decreases each iteration. Monotonic decay verified.
Multiple test runs confirm that gradient energy collapses smoothly and monotonically, with no rebounds or oscillatory behavior.
In Fourier space:
Gₖ = i k Aₖ
Thus gradient energy scales as k²|Aₖ|², causing high-frequency decay to dominate.
The gradient energy functional produces a natural gradient-descent process under diffusion.
Laplacian smoothing corresponds to exponential suppression of high-frequency gradients.
High-k components decay dramatically faster, ensuring smooth long-term structure.
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