Article citationsMore>>

Raissi, M., Perdikaris, P. and Karniadakis, G.E. (2017) Physics Informed Deep Learning (Part I): Data-Driven Solutions of Nonlinear Partial Differential Equations. arXiv:1711.10561.

has been cited by the following article:

• TITLE: Machine Learning in High-Energy Physics: Illustrative Applications in Theory and Experiment

  AUTHORS: Maggie Li, Neil Pandya

  KEYWORDS: Machine Learning, High-Energy Physics, Large Hadron Collider, Feynman Integral Reduction, Physics-Informed Neural Networks, Neural Networks, Charged Particle Tracking, Jet Tagging

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.13 No.11, November 28, 2025

  ABSTRACT: Machine learning (ML) has become an increasingly central component of high-energy physics (HEP), providing computational frameworks to address the growing complexity of theoretical calculations and experimental data at the Large Hadron Collider (LHC) and beyond. This review surveys three illustrative domains in which ML is reshaping HEP workflows: Feynman integral evaluation, charged particle tracking, and jet tagging. On the theoretical side, ML methods support more efficient integration-by-parts (IBP) reductions and approximate differential equation (DE) solutions for master integrals. In detector and classification tasks, models ranging from graph-based networks for charged particle tracking to neural networks for jet tagging demonstrate strong performance and scalability under high-luminosity conditions. Collectively, these efforts highlight the potential for ML to enhance precision, efficiency, and interpretability in particle physics and to make previously intractable problems tractable. Ongoing developments in physics-informed architectures, graph-based learning, and large-model integration may further position ML as a foundational tool for the next generation of research in HEP.