A Hybrid Framework for Healing Semigroups with Machine Learning
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Abstract (arXiv)
In this paper, we propose a hybrid framework that heals corrupted finite semigroups, combining deterministic repair strategies with Machine Learning using a Random Forest Classifier. Corruption in these tables breaks associativity and invalidates the algebraic structure. Deterministic methods work for small cardinality n and low corruption but degrade rapidly. Our experiments, carried out on Mace4-generated data sets, demonstrate that our hybrid framework achieves higher healing rates than deterministic-only and ML-only baselines. At a corruption percentage of p=15%, our framework healed 95% of semigroups up to cardinality n=6 and 60% at n=10.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 1 Sep 2025.
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References · 8
- 2023Is deep learning a useful tool for the pure mathematician?cited by 14
- 2024Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theorycited by 8
- 2025DUE: A Deep Learning Framework and Library for Modeling Unknown Equationscited by 8
- 2024Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroupscited by 7
- 2024Efficiently learning and sampling multimodal distributions with data-based initializationcited by 7
- 2025Prediction error certification for PINNs: Theory, computation, and application to Stokes flowcited by 4
- 2021A Neural Network for Semigroupscited by 2
- 2025On the contraction properties of Sinkhorn semigroupscited by 2
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