Cover Image

Learning S-Matrix Phases with Neural Operators

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Niarchos, V., Papageorgakis, C.
Format:	Preprint
Published:	2024
Subjects:	High Energy Physics - Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural Networks, Dispersion Relations and the Thermal Bootstrap
by: Niarchos, Vasilis, et al.
Published: (2026)

FeynTune: Large Language Models for High-Energy Theory
by: Richmond, Paul, et al.
Published: (2025)

Type-B Anomaly Matching and the 6D (2,0) Theory
by: Niarchos, Vasilis, et al.
Published: (2019)

Deep Finite Temperature Bootstrap
by: Niarchos, V., et al.
Published: (2025)

(Mis-)Matching Type-B Anomalies on the Higgs Branch
by: Niarchos, Vasilis, et al.
Published: (2020)

Covariantly Constant Anomalies on Conformal Manifolds
by: Andriolo, Enrico, et al.
Published: (2022)

Efficient Conformal Block Evaluation with GoBlocks
by: Chryssanthacopoulos, James, et al.
Published: (2026)

When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning
by: Niarchos, Vasilis, et al.
Published: (2026)

Neural Spectral Bias and Conformal Correlators I: Introduction and Applications
by: Ghosh, Kausik, et al.
Published: (2026)

Neural Networks Reveal a Universal Bias in Conformal Correlators
by: Ghosh, Kausik, et al.
Published: (2026)

Neural Network Learning and Quantum Gravity
by: Lanza, Stefano
Published: (2024)

Scattering, Absorption and Emission of Highly Excited Strings
by: Firrotta, Maurizio, et al.
Published: (2024)

Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory
by: Sun, Yixuan, et al.
Published: (2025)

Conformal Fields from Neural Networks
by: Halverson, James, et al.
Published: (2024)

Criticality and Saturation in Orthogonal Neural Networks
by: Guillen, Max, et al.
Published: (2026)

Rediscovery of Numerical Lüscher's Formula from the Neural Network
by: Lu, Yu, et al.
Published: (2022)

Conformal Defects in Neural Network Field Theories
by: Capuozzo, Pietro, et al.
Published: (2025)

Virasoro Symmetry in Neural Network Field Theories
by: Robinson, Brandon
Published: (2025)

Fermions and Supersymmetry in Neural Network Field Theories
by: Frank, Samuel, et al.
Published: (2025)

Detecting Homeomorphic 3-manifolds via Graph Neural Networks
by: Lawrie, Craig, et al.
Published: (2024)

Finite-Width Neural Tangent Kernels from Feynman Diagrams
by: Guillen, Max, et al.
Published: (2025)

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

A new Uncertainty Principle in Machine Learning
by: Dolotin, V., et al.
Published: (2026)

TASI Lectures on Physics for Machine Learning
by: Halverson, Jim
Published: (2024)

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)

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

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

Navigation through Non-Compact Symmetric Spaces: a mathematical perspective on Cartan Neural Networks
by: Fré, Pietro Giuseppe, et al.
Published: (2025)

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

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

The Splendors and Miseries of Heavisidisation
by: Dolotin, V., et al.
Published: (2022)

Machine Learning Toric Duality in Brane Tilings
by: Capuozzo, Pietro, et al.
Published: (2024)

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

Machine Learning the 6d Supergravity Landscape
by: Brady, Nathan, et al.
Published: (2025)

MultiSTOP: Solving Functional Equations with Reinforcement Learning
by: Trenta, Alessandro, et al.
Published: (2024)

Machine Learning Neutrino-Nucleus Cross Sections
by: Hackett, Daniel C., et al.
Published: (2024)

Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
by: Gukov, Sergei, et al.
Published: (2024)

Uncertainty Quantification From Scaling Laws in Deep Neural Networks
by: Elsharkawy, Ibrahim, et al.
Published: (2025)

AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problems
by: Jeong, Hyun-Sik, et al.
Published: (2025)