Related papers: Applications of deep learning to relativistic hydr…
In this talk we will discuss the recent advances in describing heavy-quark dynamics in the quark-gluon plasma (QGP), which evolves hydrodynamically. Special emphasis is put on the collective flow of the heavy-quarks with the medium…
One of the long-standing problems in the field of high-energy heavy-ion collisions is that the dynamical models based on viscous hydrodynamics fail to describe the experimental elliptic flow $v_2$ and the triangular flow $v_3$…
The fireball created in an ultrarelativistic heavy ion collision is the environment in which all processes providing clues about the possible formation of the quark-gluon plasma (QGP) happen. It is therefore crucial to understand the…
We simulate the space-time dynamics of high-energy collisions based on a microscopic kinetic description in the conformal relaxation time approximation, in order to determine the range of applicability of an effective description in…
A full (3+1)-dimensional calculation using the Lagrangian hydrodynamics is proposed for relativistic nuclear collisions. The calculation enables us to evaluate anisotropic flow of hadronic matter which appears in non-central and/or…
The one-dimensional non-boost-invariant evolution of the quark-gluon plasma, presumably produced during the early stages of heavy-ion collisions, is analyzed within the frameworks of viscous and anisotropic hydrodynamics. We neglect…
Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate…
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is…
We use a hydrodynamic model to study the space-time evolution transverse to the beam direction in ultrarelativistic heavy-ion collisions with nonzero impact parameters. We focus on the influence of early pressure on the development of…
The formation of quark-gluon plasma (QGP) in relativistic heavy ion collision, is expected to be accompanied by a background of ordinary collision events without phase transition. In this short note an algorithm is proposed to select the…
We propose a paradigm to deep-learn the ever-expanding databases which have emerged in mathematical physics and particle phenomenology, as diverse as the statistics of string vacua or combinatorial and algebraic geometry. As concrete…
We propose a new approach to initialize the hydrodynamic fields such as energy density distributions and four flow velocity fields in hydrodynamic modeling of high-energy nuclear collisions at the collider energies. Instead of matching the…
The success of relativistic hydrodynamics as an essential part of the phenomenological description of heavy-ion collisions at RHIC and the LHC has motivated a significant body of theoretical work concerning its fundamental aspects. Our…
Evolution of quark-gluon plasma (QGP) near equilibrium can be described by the second-order relativistic viscous hydrodynamic equations. Consistent and analytically verifiable numerical solutions are critical for phenomenological studies of…
In this article, we present a state-of-the-art algorithm for solving the relativistic viscous hydrodynamics equation with the QCD equation of state. The numerical method is based on the second-order Godunov method and has less numerical…
The discovery of the almost perfect fluid like nature of the strongly interacting quark-gluon plasma was one of the most important discoveries of heavy-ion physics in recent decades. The experimental results are well described by…
Viscous relativistic hydrodynamics in 3+1 dimensions is applied to describe heavy ion collisions at RHIC and LHC. We present calculations of observables that are sensitive to the longitudinal structure of the created system. In particular…
We developed a general deep learning framework, FluidGAN, capable of learning and predicting time-dependent convective flow coupled with energy transport. FluidGAN is thoroughly data-driven with high speed and accuracy and satisfies the…
Is a deep learning model capable of understanding systems governed by certain first principle laws by only observing the system's output? Can deep learning learn the underlying physics and honor the physics when making predictions? The…
The calculation of thermodynamic properties of biochemical systems typically requires the use of resource-intensive molecular simulation methods. One example thereof is the thermodynamic profiling of hydration sites, i.e. high-probability…