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| Formato: | Recurso digital |
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Zenodo
2025
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| Acceso en línea: | https://doi.org/10.5281/zenodo.17715490 |
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- <h3> WaterFlow v1.0.0: Initial Thermodynamic Release</h3> <p>This is the first stable release of the <strong>WaterFlow</strong> project, a novel Normalizing Flow model trained using a custom <strong>Thermodynamic Loss function ($\mathcal{F}$)</strong> and <strong>Beta-Annealing</strong> schedule.</p> <h4>Key Features & Achievements:</h4> <ol> <li><strong>Thermodynamic Training:</strong> The model successfully converges by minimizing the Helmholtz Free Energy ($\mathcal{F}$), defined as $\mathcal{F} = \beta E(x) - \lambda S_{Shannon}$. This ensures the model seeks a state of optimal performance ($E$) combined with minimal structural complexity ($S$).</li> <li><strong>Perfect Reversibility:</strong> The core Affine Coupling architecture guarantees a bijective flow. As demonstrated by the generated images, the model can perfectly invert the transformation from the latent space back to the data space.</li> <li><strong>Capillary Action Visualization:</strong> The included script performs UMAP reduction to visualize the laminar flow of data points through all layers ($X \to Z \to X$), confirming the non-turbulent, non-disipative nature of the flow.</li> </ol> <h4>Contents of this Release:</h4> <ul> <li><code>waterflow_full.py</code>: The complete training, generation, and visualization script.</li> <li><code>requirements.txt</code>: Full list of dependencies.</li> <li><code>images/</code>: Contains visualization proof (e.g., Generated Samples and UMAP Flow).</li> <li><code>LICENSE-MIT.txt</code>: For non-commercial use (research/academic).</li> <li><code>LICENSE-COMMERCIAL.txt</code>: Placeholder for commercial licensing.</li> </ul> <p>We welcome academic researchers to utilize this framework under the MIT License.</p>