A survey of machine learning-based physics event generation

no code implementations • 1 Jun 2021 • Yasir Alanazi, N. Sato, Pawel Ambrozewicz, Astrid N. Hiller Blin, W. Melnitchouk, Marco Battaglieri, Tianbo Liu, Yaohang Li

Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions.

BIG-bench Machine Learning Super-Resolution

Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

no code implementations • 29 Jan 2020 • Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Pawel Ambrozewicz, Florian Hauenstein, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics.

Generative Adversarial Network

Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

no code implementations • 24 Sep 2017 • Siyi Li, Tianbo Liu, Chi Zhang, Dit-yan Yeung, Shaojie Shen

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process.

reinforcement-learning Reinforcement Learning (RL)