rdt-kernel 1.0.1

Recursive Diffusion-Type (RDT) Kernel — entropy-regulated nonlinear diffusion operator in PyTorch.

RDT Kernel

Recursive Diffusion-Type Kernel (RDT)
Author: Steven Reid (RRG314 Research Group)
License: MIT

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Overview

The RDT Kernel is a nonlinear, entropy-regulated diffusion operator implemented in PyTorch.
It provides a compact and efficient way to evolve scalar fields under a coupled logarithmic potential and Laplacian diffusion dynamic:

\ \frac{\partial L}{\partial t} = -\alpha \log(L) + D \nabla^2 L \

Unlike conventional linear diffusion equations, the RDT Kernel introduces a logarithmic potential term that regulates field entropy, maintaining bounded and stable evolution even in recursive or chaotic systems.
It is lightweight, numerically stable, and compatible with CPU, GPU, and TPU backends.

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Key Features

• Cross-hardware operation (CPU, GPU, TPU)
• Entropy-regulated diffusion with a logarithmic potential
• Fully parallelized PyTorch implementation
• Differentiable and compatible with machine-learning workflows
• Guaranteed positivity and bounded energy through clamping
• Minimal dependencies (requires only torch; optional torch_xla for TPU)

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Typical Applications

Domain	Example Uses
Machine Learning / AI	Physics-informed neural networks, differentiable PDE layers, generative or diffusion models
Scientific Computing	Nonlinear diffusion and stability simulations, entropy-bounded PDE solvers
Computational Physics	Entropy-driven field relaxation, recursive geometric entropy studies
Signal & Image Processing	Nonlinear smoothing and denoising, texture evolution
Information Theory	Modeling entropy growth and information diffusion

Because it is differentiable and hardware-agnostic, the RDT Kernel can be integrated directly into PyTorch autograd graphs for end-to-end training of PDE-based or hybrid AI-physics systems.

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Mathematical Foundation

The RDT equation combines two terms:

1. Logarithmic Entropy Term
(-α log L) — acts as a restoring potential that resists uncontrolled growth and encodes entropy regulation.

2. Diffusion Term
(D ∇² L) — implements standard Laplacian smoothing, spreading gradients spatially to stabilize field variations.

Together these form a self-stabilizing PDE suitable for equilibrium-seeking and diffusion-reaction systems.

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API Reference

get_device()

Detects the best compute backend.

device, name = get_device()
# returns ("cuda", "GPU"), ("cpu", "CPU"), or ("TPU")

rdt_kernel(L, alpha=0.5, D=0.1, dx=1.0)

Computes the instantaneous field derivative:

Parameter	Type	Description
L	Tensor	Input scalar field (must be positive)
alpha	float	Logarithmic potential coefficient
D	float	Diffusion coefficient
dx	float	Spatial step size

Returns: Tensor — field derivative (∂L/∂t)

step(L, alpha=0.5, D=0.1, dx=1.0, dt=0.01)

Advances the field one time step using:

\ L_{t+1} = L_t + \Delta t \cdot \text{RDT}(L_t) \

The output is clamped to remain positive and bounded.

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Quick Start

import torch
from rdt_kernel import step, get_device

device, name = get_device()
print("Running on", name)

L = (1 + 0.1 * torch.rand((1, 256, 256))).to(device)

for _ in range(100):
    L = step(L, alpha=0.5, D=0.1, dx=1.0, dt=0.01)

print("Mean field value:", L.mean().item())

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Integration Examples

As a Differentiable Layer

import torch.nn as nn
from rdt_kernel import step

class RDTLayer(nn.Module):
    def __init__(self, alpha=0.5, D=0.1, dx=1.0, dt=0.01):
        super().__init__()
        self.alpha, self.D, self.dx, self.dt = alpha, D, dx, dt

    def forward(self, L):
        return step(L, self.alpha, self.D, self.dx, self.dt)

In a Physics Simulation Pipeline

from torchdiffeq import odeint
from rdt_kernel import rdt_kernel
import torch

t = torch.linspace(0, 1, 100)
L0 = torch.ones((1, 128, 128))
sol = odeint(lambda t, L: rdt_kernel(L), L0, t)

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Performance

• Efficient for large grids (512×512 and higher)
• Linear memory scaling on GPU
• TPU-compatible via torch_xla
• Numerically stable for dt ≤ 0.05 in most scenarios

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Installation

From GitHub

git clone https://github.com/RRG314/rdt-kernel.git
cd rdt-kernel
pip install .

From PyPI (when available)

pip install rdt-kernel

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Requirements

• Python ≥ 3.8
• PyTorch ≥ 1.12
• Optional: torch_xla for TPU support

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Research Context

The RDT Kernel originated in the Recursive Geometric Entropy and Entropy Field Theory (REFT) framework.
It provides a minimal computational mechanism for entropy-bounded field evolution—relevant to stability analysis, information diffusion, and recursive geometric systems.

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Roadmap

• Add 3-D and multi-dimensional variants
• Benchmark mixed-precision (FP16/BF16)
• Add symbolic auto-differentiation module
• Publish official PyPI distribution

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License

MIT License © 2025 Steven Reid
Permission is granted to use, copy, modify, and distribute this software with attribution.

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A lightweight, entropy-regulated diffusion operator bridging physics and computation.