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Analysis

This paper introduces a novel 4D spatiotemporal formulation for solving time-dependent convection-diffusion problems. By treating time as a spatial dimension, the authors reformulate the problem, leveraging exterior calculus and the Hodge-Laplacian operator. The approach aims to preserve physical structures and constraints, leading to a more robust and potentially accurate solution method. The use of a 4D framework and the incorporation of physical principles are the key strengths.
Reference

The resulting formulation is based on a 4D Hodge-Laplacian operator with a spatiotemporal diffusion tensor and convection field, augmented by a small temporal perturbation to ensure nondegeneracy.

Deep Learning for Parton Distribution Extraction

Published:Dec 25, 2025 18:47
1 min read
ArXiv

Analysis

This paper introduces a novel machine-learning method using neural networks to extract Generalized Parton Distributions (GPDs) from experimental data. The method addresses the challenging inverse problem of relating Compton Form Factors (CFFs) to GPDs, incorporating physical constraints like the QCD kernel and endpoint suppression. The approach allows for a probabilistic extraction of GPDs, providing a more complete understanding of hadronic structure. This is significant because it offers a model-independent and scalable strategy for analyzing experimental data from Deeply Virtual Compton Scattering (DVCS) and related processes, potentially leading to a better understanding of the internal structure of hadrons.
Reference

The method constructs a differentiable representation of the Quantum Chromodynamics (QCD) PV kernel and embeds it as a fixed, physics-preserving layer inside a neural network.