Search Results for author:
Found 32 papers, 5 papers with code
Learning to be Simple
In this work we employ machine learning to understand structured mathematical data involving finite groups and derive a theorem about necessary properties of generators of finite simple groups.
Machine Learning Clifford invariants of ADE Coxeter elements
This provides the perfect setup for machine learning, and indeed we see that the datasets can be machine learned to very high accuracy.
Flowers of immortality
To address this, we recently derived a mortality equation that governs the transition matrix of an evolving population with a given maximum age.
Machine Learning Class Numbers of Real Quadratic Fields
We implement and interpret various supervised learning experiments involving real quadratic fields with class numbers 1, 2 and 3.
Topological data analysis on noisy quantum computers
In this study, we present NISQ-TDA, a fully implemented end-to-end quantum machine learning algorithm needing only a short circuit-depth, that is applicable to high-dimensional classical data, and with provable asymptotic speedup for certain classes of problems.
Machine Learning Algebraic Geometry for Physics
We review some recent applications of machine learning to algebraic geometry and physics.
Murmurations of elliptic curves
We investigate the average value of the $p$th Dirichlet coefficients of elliptic curves for a prime p in a fixed conductor range with given rank.
Cluster Algebras: Network Science and Machine Learning
Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types.
From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook
We review the recent programme of using machine-learning to explore the landscape of mathematical problems.
Machine-Learning the Classification of Spacetimes
On the long-established classification problems in general relativity we take a novel perspective by adopting fruitful techniques from machine learning and modern data-science.
Calabi-Yau Metrics, Energy Functionals and Machine-Learning
We apply machine learning to the problem of finding numerical Calabi-Yau metrics.
Machine Learning Calabi-Yau Hypersurfaces
We revisit the classic database of weighted-P4s which admit Calabi-Yau 3-fold hypersurfaces equipped with a diverse set of tools from the machine-learning toolbox.
The World in a Grain of Sand: Condensing the String Vacuum Degeneracy
We propose a novel approach toward the vacuum degeneracy problem of the string landscape, by finding an efficient measure of similarity amongst compactification scenarios.
Neurons on Amoebae
We apply methods of machine-learning, such as neural networks, manifold learning and image processing, in order to study 2-dimensional amoebae in algebraic geometry and string theory.
Machine-Learning Mathematical Structures
We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years.
Machine-Learning Arithmetic Curves
We show that standard machine-learning algorithms may be trained to predict certain invariants of low genus arithmetic curves.
Machine-Learning Number Fields
We show that standard machine-learning algorithms may be trained to predict certain invariants of algebraic number fields to high accuracy.
Machine Learning Lie Structures & Applications to Physics
Classical and exceptional Lie algebras and their representations are among the most important tools in the analysis of symmetry in physical systems.
Machine-Learning the Sato--Tate Conjecture
Throughout, our observations are verified using known results from the literature and the data available in the LMFDB.
Chiral Rings, Futaki Invariants, Plethystics, and Groebner Bases
We study chiral rings of 4d $\mathcal{N}=1$ supersymmetric gauge theories via the notion of K-stability.
High Energy Physics - Theory Algebraic Geometry
Machine Learning Calabi-Yau Four-folds
Hodge numbers of Calabi-Yau manifolds depend non-trivially on the underlying manifold data and they present an interesting challenge for machine learning.
Graph Laplacians, Riemannian Manifolds and their Machine-Learning
Graph Laplacians as well as related spectral inequalities and (co-)homology provide a foray into discrete analogues of Riemannian manifolds, providing a rich interplay between combinatorics, geometry and theoretical physics.
Machine-Learning Dessins d'Enfants: Explorations via Modular and Seiberg-Witten Curves
We apply machine-learning to the study of dessins d'enfants.
Machine Learning String Standard Models
We study machine learning of phenomenologically relevant properties of string compactifications, which arise in the context of heterotic line bundle models.
Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer
Empirical analysis is often the first step towards the birth of a conjecture.
Machine learning Calabi-Yau metrics
We apply machine learning to the problem of finding numerical Calabi-Yau metrics.
Dynamics of genetic code evolution: The emergence of universality
We study the dynamics of genetic code evolution.
Learning Algebraic Structures: Preliminary Investigations
We employ techniques of machine-learning, exemplified by support vector machines and neural classifiers, to initiate the study of whether AI can "learn" algebraic structures.
Machine-learning a virus assembly fitness landscape
Realistic evolutionary fitness landscapes are notoriously difficult to construct.
The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
We present a pedagogical introduction to the recent advances in the computational geometry, physical implications, and data science of Calabi-Yau manifolds.
Machine Learning CICY Threefolds
The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building.
Deep-Learning the Landscape
We propose a paradigm to deep-learn the ever-expanding databases which have emerged in mathematical physics and particle phenomenology, as diverse as the statistics of string vacua or combinatorial and algebraic geometry.
