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String Model Building, Reinforcement Learning and Genetic Algorithms
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We investigate reinforcement learning and genetic algorithms in the context of heterotic Calabi-Yau models with monad bundles. Both methods are found to be highly efficient in identifying phenomenologically attractive three-family models, in cases where systematic scans are not feasible. For monads on the bi-cubic Calabi-Yau either method facilitates a complete search of the environment and leads to similar sets of previously unknown three-family models.
Forward citations
Cited by 2 Pith papers
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Pre-Strings Lectures on Artificial Intelligence
Lecture notes define neural-network field theory and survey how it recovers known QFT/string results plus applied AI techniques for string problems.
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Toward Supporting Narrative-Driven Data Exploration: Barriers and Design Opportunities
Clustering particles by mass, spin, lifetime and decay modes with conventional tools reproduces known Standard Model groupings, but the dataset and algorithm choices quietly encode the theory being 'rediscovered'.
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