Evolving neural networks with genetic algorithms to study the String Landscape

Abstract

We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.

17 pages, 7 figures, references added, typos corrected, extended introductory section

References in corpus (16)

• The statistics of string/M theory vacua
• Exploring Positive Monad Bundles And A New Heterotic Standard Model
• Heterotic Compactification, An Algorithmic Approach
• A Comprehensive Scan for Heterotic SU(5) GUT models
• Toric bases for 6D F-theory models
• Monad Bundles in Heterotic String Compactifications
• All Complete Intersection Calabi-Yau Four-Folds
• Brane Brick Models in the Mirror
• A New Construction of Calabi-Yau Manifolds: Generalized CICYs
• Genetic Algorithms and the Search for Viable String Vacua
• Genetic Algorithms and Experimental Discrimination of SUSY Models
• Heterotic Models from Vector Bundles on Toric Calabi-Yau Manifolds
• Geography of Fields in Extra Dimensions: String Theory Lessons for Particle Physics
• A New Method for Finding Vacua in String Phenomenology
• The Moduli Space of Heterotic Line Bundle Models: a Case Study for the Tetra-Quadric
• On the Classification of Brane Tilings

Cited by in corpus (61)

• Machine Learning in the String Landscape
• Deep Learning and AdS/CFT
• The Tadpole Problem
• Machine Learning CICY Threefolds
• Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
• A Systematic Approach to Kähler Moduli Stabilisation
• AdS/CFT as a deep Boltzmann machine
• Machine learning Calabi-Yau metrics
• Searching the Landscape of Flux Vacua with Genetic Algorithms
• Deep Learning and AdS/QCD
• Machine Learning Line Bundle Cohomology
• Computational Complexity of Vacua and Near-Vacua in Field and String Theory
• Getting CICY High
• Moduli Stabilization in Asymptotic Flux Compactifications
• Vacuum Selection from Cosmology on Networks of String Geometries
• Quiver Mutations, Seiberg Duality and Machine Learning
• Machine Learning Calabi-Yau Hypersurfaces
• Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
• Estimating Calabi-Yau Hypersurface and Triangulation Counts with Equation Learners
• Explore and Exploit with Heterotic Line Bundle Models
• Machine-Learning Dessins d'Enfants: Explorations via Modular and Seiberg-Witten Curves
• Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
• Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
• Formulae for Line Bundle Cohomology on Calabi-Yau Threefolds
• On Axion Reheating in the String Landscape
• Deep learning and k-means clustering in heterotic string vacua with line bundles
• Statistical Predictions in String Theory and Deep Generative Models
• Accessibility Measure for Eternal Inflation: Dynamical Criticality and Higgs Metastability
• Moduli-dependent KK towers and the swampland distance conjecture on the quintic Calabi-Yau manifold
• Neural Networks Optimized by Genetic Algorithms in Cosmology
• Machine Learning and Algebraic Approaches towards Complete Matter Spectra in 4d F-theory
• Learning non-Higgsable gauge groups in 4D F-theory
• Artificial Neural Network in Cosmic Landscape
• Calabi-Yau Metrics, Energy Functionals and Machine-Learning
• Line bundle cohomologies on CICYs with Picard number two
• Deep multi-task mining Calabi-Yau four-folds
• Applying machine learning to the Calabi-Yau orientifolds with string vacua
• The landscape of promising non-supersymmetric string models
• Breeding realistic D-brane models
• Kahler Moduli Stabilization and the Propagation of Decidability
• Disentangling a Deep Learned Volume Formula
• A Triumvirate of AI Driven Theoretical Discovery
• 134 Billion Intersecting Brane Models
• Predicting the orbifold origin of the MSSM
• Unsupervised Machine Learning Techniques for Exploring Tropical Coamoeba, Brane Tilings and Seiberg Duality
• Ising Machines for Diophantine Problems in Physics
• Machine learning Sasakian and $G_2$ topology on contact Calabi-Yau $7$-manifolds
• Learning to Inflate
• Polytopes and Machine Learning
• Machine Learning Regularization for the Minimum Volume Formula of Toric Calabi-Yau 3-folds
• Characterizing 4-string contact interaction using machine learning
• Level Crossings, Attractor Points and Complex Multiplication
• Universes as Big Data
• Learning BPS Spectra and the Gap Conjecture
• Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
• Generative AI for Brane Configurations and Coamoeba
• Black holes and the loss landscape in machine learning
• Calabi-Yau Four/Five/Six-folds as ${\mathbb{P}}_{\text{w}}^n$ Hypersurfaces: Machine Learning, Approximation, and Generation
• Row and column detection complexities of character tables
• evortran: a modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction
• Learning 3-Manifold Triangulations