DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks

Giovanni Ruggieri

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arXiv · 2507.00101

DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks

Giovanni Ruggieri

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Abstract

We introduce DFReg, a physics-inspired regularization method for deep neural networks that operates on the global distribution of weights. Drawing from Density Functional Theory (DFT), DFReg applies a functional penalty to encourage smooth, diverse, and well-distributed weight configurations. Unlike traditional techniques such as Dropout or L2 decay, DFReg imposes global structural regularity without architectural changes or stochastic perturbations.

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Giovanni Ruggieri. 2025-06-30. DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks. https://arxiv.org/abs/2507.00101

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