1 citations · 2 across the 9 of their papers we have counts for
10 papers
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
Jing-Zong Zhang, Wang-Mei Zha, Lingxiao Wang +1
Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral c…
Generative Criticality in Large Language Model Temperature Scaling
Huajian Ruan, Jinyang Li, Xingyu Guo +1
We propose a statistical-field framework for text generated by large language models (LLMs), treating token embeddings as continuous spin variables on a one-dimensional chain. Defi…
Reconstruction of fast-rotating neutron star observables with the neural network
Wen Liu, Lingxiao Wang, Zhenyu Zhu
Rotation can significantly affect neutron-star (NS) properties, but accurate modeling of rapidly rotating NSs requires solving a two-dimensional, axially symmetric system, making t…
Learning Quantum Operator Dynamics from Short-Time Data
Jinyang Li, Satoshi Iso, Shunji Matsuura +2
Real-time dynamics of quantum observables provide direct access to excitation spectra and correlation functions in quantum many-body systems, but currently available quantum device…
Solving Functional Renormalization Group Equations with Neural Networks
Yang-yang Tan, Wei-jie Fu, Lianyi He +1
We employ deep neural networks to represent the field derivative of the scale-dependent effective potential in the functional renormalization group (fRG) framework for nonperturbat…
HoloNet: Toward a Unified Einstein-Maxwell-Dilaton Framework of QCD
Hong-An Zeng, Lingxiao Wang, Mei Huang
We propose HoloNet, a neural-network framework that unifies lattice QCD(LQCD) thermodynamics and holographic Einstein-Maxwell-Dilaton (EMD) theory within a data-to-holography pipel…