49 citations · 153 across the 17 of their papers we have counts for
17 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…
Deep learning for exploring hadron-hadron interactions
Lingxiao Wang
In this proceeding, we introduce deep learning technologies for studying hadron-hadron interactions. To extract parameterized hadron interaction potentials from collision experimen…
Learning Hadron Emitting Sources with Deep Neural Networks
Lingxiao Wang, Jiaxing Zhao
The correlation function observed in high-energy collision experiments encodes critical information about the emitted source and hadronic interactions. While the proton-proton inte…
Building Hadron Potentials from Lattice QCD with Deep Neural Networks
Lingxiao Wang, Takumi Doi, Tetsuo Hatsuda +1
In this study, we develop a deep learning method to learn hadronic interactions unsupervisedly from the correlation functions calculated in lattice QCD simulations. We present our…
High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite
Haixia Xiao, Feng Zhang, Lingxiao Wang +6
Clouds play a critical role in Earth's hydrological and energy cycles, and accurately representing their properties is essential for effective numerical modeling and weather foreca…
Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help
Shuzhe Shi, Lingxiao Wang, Kai Zhou
Reconstructing hadron spectral functions through Euclidean correlation functions are of the important missions in lattice QCD calculations. However, in a Källen--Lehmann(KL) spectr…
Neural network reconstruction of the dense matter equation of state from neutron star observables
Shriya Soma, Lingxiao Wang, Shuzhe Shi +2
The Equation of State (EoS) of strongly interacting cold and hot ultra-dense QCD matter remains a major challenge in the field of nuclear astrophysics. With the advancements in mea…
Automatic differentiation approach for reconstructing spectral functions with neural networks
Lingxiao Wang, Shuzhe Shi, Kai Zhou
Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in physics. The prior knowledge for specific physical systems routinely offers es…
Reconstructing spectral functions via automatic differentiation
Lingxiao Wang, Shuzhe Shi, Kai Zhou
Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in many-body physics. However, the inversion is proved to be ill-posed in the rea…
Learning Langevin dynamics with QCD phase transition
Lingxiao Wang, Lijia Jiang, Kai Zhou
In this proceeding, the deep Convolutional Neural Networks (CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin proce…
Detecting Chiral Magnetic Effect via Deep Learning
Yuan-Sheng Zhao, Lingxiao Wang, Kai Zhou +1
The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to…
Measuring the rationality in evacuation behavior with deep learning
Huaidian Hou, Lingxiao Wang
The bounded rationality is a crucial component in human behaviors. It plays a key role in the typical collective behavior of evacuation, in which the heterogeneous information lead…