81 citations · 275 across the 19 of their papers we have counts for
4 papers · 1 filter
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.…