81 citations · 275 across the 19 of their papers we have counts for
19 papers
Combining complex Langevin dynamics with score-based and energy-based diffusion models
Gert Aarts, Diaa E. Habibi, Lingxiao Wang +1
Theories with a sign problem due to a complex action or Boltzmann weight can sometimes be numerically solved using a stochastic process in the complexified configuration space. How…
Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
Shuang Guo, Lingxiao Wang, Kai Zhou +1
The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning…
Physics-Conditioned Diffusion Models for Lattice Gauge Theory
Qianteng Zhu, Gert Aarts, Wei Wang +2
We develop diffusion models for simulating lattice gauge theories, where stochastic quantization is explicitly incorporated as a physical condition for sampling. We demonstrate the…
Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis
Ronghao Li, Sophia Han, Zidu Lin +3
The potential hadron-to-quark phase transition in neutron stars has not been fully understood as the property of cold, dense, and strongly interacting matter cannot be theoreticall…
Diffusion models and stochastic quantisation in lattice field theory
Gert Aarts, Lingxiao Wang, Kai Zhou
Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochast…
Diffusion models learn distributions generated by complex Langevin dynamics
Diaa E. Habibi, Gert Aarts, Lingxiao Wang +1
The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion…
Diffusion models for lattice gauge field simulations
Qianteng Zhu, Gert Aarts, Wei Wang +2
We develop diffusion models for lattice gauge theories which build on the concept of stochastic quantization. This framework is applied to gauge theory in dimensions.…
On learning higher-order cumulants in diffusion models
Gert Aarts, Diaa E. Habibi, Lingxiao Wang +1
To analyse how diffusion models learn correlations beyond Gaussian ones, we study the behaviour of higher-order cumulants, or connected n-point functions, under both the forward an…
Building imaginary-time thermal field theory with artificial neural networks
Tian Xu, Lingxiao Wang, Lianyi He +2
In this study, we introduce a novel approach in quantum field theories to estimate the action using the artificial neural networks (ANNs). The estimation is achieved by learning on…
Generative Diffusion Models for Lattice Field Theory
Lingxiao Wang, Gert Aarts, Kai Zhou
This study delves into the connection between machine learning and lattice field theory by linking generative diffusion models (DMs) with stochastic quantization, from a stochastic…
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Lingxiao Wang, Gert Aarts, Kai Zhou
In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ). The DM is realized by approximating the reversal of a sto…
Exploring QCD matter in extreme conditions with Machine Learning
Kai Zhou, Lingxiao Wang, Long-Gang Pang +1
In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interac…
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
Shuai Han, Lukas Stelz, Horst Stoecker +2
A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases.…
Amplitude/Phase Retrieval for Terahertz Holography with Supervised and Unsupervised Physics-Informed Deep Learning
Mingjun Xiang, Hui Yuan, Lingxiao Wang +2
Recently, digital holographic imaging techniques (including methods with heterodyne detection) have found increased attention in the terahertz (THz) frequency range. However, holog…
Fourier-Flow model generating Feynman paths
Shile Chen, Oleh Savchuk, Shiqi Zheng +4
As an alternative but unified and more fundamental description for quantum physics, Feynman path integrals generalize the classical action principle to a probabilistic perspective,…
Reconstructing the neutron star equation of state from observational data via automatic differentiation
Shriya Soma, Lingxiao Wang, Shuzhe Shi +2
Neutron star observables like masses, radii, and tidal deformability are direct probes to the dense matter equation of state~(EoS). A novel deep learning method that optimizes an E…
A Multi-user Oriented Live Free-viewpoint Video Streaming System Based On View Interpolation
Jingchuan Hu, Shuai Guo, Kai Zhou +3
As an important application form of immersive multimedia services, free-viewpoint video(FVV) enables users with great immersive experience by strong interaction. However, the compu…
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…