:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cao, Haotian, Merz, Garrett, Cranmer, Kyle, Shiu, Gary
Format:	Preprint
Published:	2026
Subjects:	High Energy Physics - Theory Machine Learning
Online Access:	https://arxiv.org/abs/2605.01072
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory
by: Cai, Tianji, et al.
Published: (2024)

Recurrent Features of Amplitudes in Planar $\mathcal{N}=4$ Super Yang-Mills Theory
by: Cai, Tianji, et al.
Published: (2025)

Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers
by: Yip, Jacky H. T., et al.
Published: (2025)

Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function
by: Drnevich, Matthew, et al.
Published: (2025)

Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data
by: Drnevich, Matthew, et al.
Published: (2024)

Schwinger Effect of Extremal Reissner-Nordström Black Holes
by: Lin, Puxin, et al.
Published: (2024)

Self-Supervised Learning Strategies for Jet Physics
by: Rieck, Patrick, et al.
Published: (2025)

The Standard Model from String Theory: What Have We Learned?
by: Marchesano, Fernando, et al.
Published: (2024)

Graph theoretical approach to conformal correlators: The conformal squid
by: Irges, Nikos, et al.
Published: (2024)

Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
by: Gandrakota, Abhijith, et al.
Published: (2024)

Connecting flux vacua through scalar field excursions
by: Shiu, Gary, et al.
Published: (2023)

Quantum-classical simulation of quantum field theory by quantum circuit learning
by: Ikeda, Kazuki
Published: (2023)

Emergent field theories from neural networks
by: Vanchurin, Vitaly
Published: (2024)

Symmetry Breaking in Transformers for Efficient and Interpretable Training
by: Silverstein, Eva, et al.
Published: (2026)

Learning the Inverse Ryu--Takayanagi Formula with Transformers
by: Kim, Sejin
Published: (2025)

Viability of perturbative expansion for quantum field theories on neurons
by: Sen, Srimoyee, et al.
Published: (2025)

Expanding the Weak Gravity Conjecture in AdS Space
by: Lin, Puxin, et al.
Published: (2025)

Formulating the Weak Gravity Conjecture in AdS Space
by: Lin, Puxin, et al.
Published: (2025)

Transformer models are gauge invariant: A mathematical connection between AI and particle physics
by: van Nierop, Leo
Published: (2024)

Bounding axion dark energy
by: Shiu, Gary, et al.
Published: (2026)

Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
by: Hackett, Daniel C., et al.
Published: (2021)

Long-lived SEC violation via DM/DE couplings
by: Shiu, Gary, et al.
Published: (2025)

Learning the S-matrix from data: Rediscovering gravity from gauge theory via symbolic regression
by: Moynihan, Nathan
Published: (2026)

Descending into the Modular Bootstrap
by: Benjamin, Nathan, et al.
Published: (2026)

Learning to Unscramble Feynman Loop Integrals with SAILIR
by: Shih, David
Published: (2026)

Feature Learning and Generalization in Deep Networks with Orthogonal Weights
by: Day, Hannah, et al.
Published: (2023)

Stochastic normalizing flows for Effective String Theory
by: Caselle, Michele, et al.
Published: (2024)

Lecture Notes on Normalizing Flows for Lattice Quantum Field Theories
by: Cheng, Miranda C. N., et al.
Published: (2025)

A Perspective on Symbolic Machine Learning in Physical Sciences
by: Makke, Nour, et al.
Published: (2025)

Learning the Simplicity of Scattering Amplitudes
by: Cheung, Clifford, et al.
Published: (2024)

Exploring the flavor structure of quarks and leptons with reinforcement learning
by: Nishimura, Satsuki, et al.
Published: (2023)

Good flavor search in SU(5): a machine learning approach
by: Abu-Ajamieh, Fayez, et al.
Published: (2025)

Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
by: Caselle, Michele, et al.
Published: (2023)

Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning
by: von Hippel, Matt, et al.
Published: (2025)

Truth, beauty, and goodness in grand unification: a machine learning approach
by: Kawai, Shinsuke, et al.
Published: (2024)

Numerical determination of the width and shape of the effective string using Stochastic Normalizing Flows
by: Caselle, Michele, et al.
Published: (2024)

cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature
by: Berglund, Per, et al.
Published: (2024)

Studying Effective String Theory using deep generative models
by: Caselle, Michele, et al.
Published: (2025)

Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics
by: Terin, Rodrigo Carmo
Published: (2024)

Reinforcement learning-based statistical search strategy for an axion model from flavor
by: Nishimura, Satsuki, et al.
Published: (2024)