Deep Learning Methods for Symbolic Calculations in HEP

doi:10.2172/3363795

DOE OSTI · 3363795

Deep Learning Methods for Symbolic Calculations in HEP

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Abstract

This project develops machine learning methods to accelerate symbolic calculations in high-energy physics. Using sequence-to-sequence transformer models, we construct frameworks to predict squared amplitudes and related quantities for Standard Model processes, including quantum electrodynamics, quantum chromodynamics, and electroweak interactions. The results demonstrate that deep learning can successfully learn complex symbolic relationships and provide a scalable approach to symbolic computation with potential applications in precision calculations and collider phenomenology.

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS high-energy physics machine learning quantum field theory scattering amplitudes symbolic computation transformer models

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2026-05-15. Deep Learning Methods for Symbolic Calculations in HEP. https://doi.org/10.2172/3363795

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