Machine learning modularity

Abstract:Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine learning framework for automatically simplifying complex expressions involving multiple elliptic Gamma functions, including the $q$-$\theta$ function and the elliptic Gamma function. The model learns to apply algebraic identities, particularly the SL$(2,\mathbb{Z})$ and SL$(3,\mathbb{Z})$ modular transformations, to reduce heavily scrambled expressions to their canonical forms. Experimental results show that the model achieves over 99\% accuracy on in-distribution tests and maintains robust performance (exceeding 90\% accuracy) under significant extrapolation, such as with deeper scrambling depths. This demonstrates that the model has internalized the underlying algebraic rules of modular transformations rather than merely memorizing training patterns. Our work presents the first successful application of machine learning to perform symbolic simplification using modular identities, offering a new automated tool for computations with special functions in quantum field theory and the string theory.

High Energy Physics - Theory (hep-th); Machine Learning (cs.LG)

arXiv:2601.01779 [hep-th]

(or arXiv:2601.01779v2 [hep-th] for this version)

https://doi.org/10.48550/arXiv.2601.01779 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1103/j99v-l6zq Focus to learn more DOI(s) linking to related resources

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