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Found 8 papers, 0 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.
Mathematical conjecture generation using machine intelligence
Finally, we propose a pipeline of mathematical discovery in this space and highlight the importance of domain expertise in this pipeline.
A physics-informed search for metric solutions to Ricci flow, their embeddings, and visualisation
Neural networks with PDEs embedded in their loss functions (physics-informed neural networks) are employed as a function approximators to find solutions to the Ricci flow (a curvature based evolution) of Riemannian metrics.
Machine Learned Calabi-Yau Metrics and Curvature
Finding Ricci-flat (Calabi-Yau) metrics is a long standing problem in geometry with deep implications for string theory and phenomenology.
Machine Learning for Optical Motion Capture-driven Musculoskeletal Modelling from Inertial Motion Capture Data
Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into in vivo joint and muscle loading, aiding clinical decision-making.
Neural Network Approximations for Calabi-Yau Metrics
Ricci flat metrics for Calabi-Yau threefolds are not known analytically.
Baryons from Mesons: A Machine Learning Perspective
Quantum chromodynamics (QCD) is the theory of the strong interaction.
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.
