Deep learning stochastic processes with QCD phase transition

Chiral Phase Transition in QCD

We employ non–perturbative flow equations to compute the equation of state for two flavor QCD within an effective quark meson model. This yields the temperature and quark mass dependence of quantities like the chiral condensate or the pion mass. Our treatment covers both the chiral perturbation theory domain of validity and the domain of validity of universality associated with critical phenome...

Two Flavour QCD Phase Transition

Results on the phase transition in QCD with two flavours of light staggered fermions from an ongoing simulation are presented. We find the restoration of the chiral SU(2)× SU(2) symmetry, but not of the axial UA(1) symmetry. 1 Deconfinement and Chiral Symmetry There are two symmetries in QCD, each giving rise to a phase transition. For infinite quark mass, ie., pure SU(3) gauge theory, there is...

Deconfinement Phase Transition in QCD

Stochastic phase transition operator.

In this study a Markov operator is introduced that represents the density evolution of an impulse-driven stochastic biological oscillator. The operator's stochastic kernel is constructed using the asymptotic expansion of stochastic processes instead of solving the Fokker-Planck equation. The Markov operator is shown to successfully approximate the density evolution of the biological oscillator ...

Modeling Dynamics with Deep Transition-Learning Networks

Markov processes, both classical and higher order, are often used to model dynamic processes, such as stock prices, molecular dynamics, and Monte Carlo methods. Previous works have shown that an autoencoder can be formulated as a specific type of Markov chain. Here, we propose a generative neural network known as a transition encoder, or transcoder, which learns such continuous-state dynamic pr...