Related papers: Applications of deep learning to relativistic hydr…
We introduce a coarse-grained deep neural network model (CG-DNN) for liquid water that utilizes 50 rotational and translational invariant coordinates, and is trained exclusively against energies of ~30,000 bulk water configurations. Our…
Short-term water demand forecasting (StWDF) is the foundation stone in the derivation of an optimal plan for controlling water supply systems. Deep learning (DL) approaches provide the most accurate solutions for this purpose. However, they…
High-energy nuclear collisions exhibit collective flow, which emerges as a dynamical response of the Quark-Gluon Plasma (QGP) to the initial state geometry of the collision. Collective flow in heavy-ion collisions is usually described…
Due to large pressure gradients at early times, standard hydrodynamic model simulations of relativistic heavy-ion collisions do not become reliable until $O(1)$\,fm/$c$ after the collision. To address this one often introduces a…
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encapsulate the hidden, Lagrangian vortical evolution underpinning…
The pre-equilibrium evolution of a quark-gluon plasma produced in a heavy-ion collision is studied in the framework of kinetic theory. We discuss the approach to local thermal equilibrium, and the onset of hydrodynamics, in terms of a…
The far-from equilibrium dynamics of the pre-hydrodynamic quark-gluon plasma (QGP) formed in heavy ion collisions can be characterized by distinct stages, during each of which the system loses some memory of its initial condition, until…
We present an efficient deep learning technique for the model reduction of the Navier-Stokes equations for unsteady flow problems. The proposed technique relies on the Convolutional Neural Network (CNN) and the stochastic gradient descent…
Ultrarelativistic collisions between heavy nuclei briefly generate the quark-gluon plasma (QGP), a new state of matter characterized by deconfined partons last seen microseconds after the Big Bang. The properties of the QGP are of intense…
A new set of equations for relativistic viscous hydrodynamics that captures both weak-coupling and strong-coupling physics to second order in gradients has been developed recently. We apply this framework to bulk physics at RHIC, both for…
Accurately predicting turbulent flows remains a central challenge in fluid dynamics due to their high dimensionality and intrinsic nonlinearity. Recent developments in quantum algorithms and machine learning offer new opportunities for…
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark…
We use covariant methods to analyse the nonlinear evolution of self-gravitating, non-relativistic media. The formalism is first applied to imperfect fluids, aiming at the kinematic effects of viscosity, before extended to inhomogeneous…
The transport properties of the strongly-coupled quark-gluon plasma created in ultra-relativistic heavy-ion collisions are extracted by Bayesian parameter estimate methods with the latest collision beam energy data from LHC. This Bayesian…
Relativistic heavy ion collisions have reached energies that enable the creation of a novel state of matter termed the quark-gluon plasma. Many observables point to a picture of the medium as rapidly equilibrating and expanding as a nearly…
Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields. However, the modeling of physical processes from simulation…
Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction…
We attempt to model Quark Gluon Plasma (QGP) evolution from the initial Heavy Ion collision to the final hadronic gas state by combining the Glauber model initial state conditions with eccentricity fluctuations, pre-equilibrium flow, UVH2+1…
The second-order hydrodynamic equations for evolution of shear and bulk viscous pressure have been derived within the framework of covariant kinetic theory based on the effective fugacity quasiparticle model. The temperature-dependent…
Traditional fluid flow predictions require large computational resources. Despite recent progress in parallel and GPU computing, the ability to run fluid flow predictions in real-time is often infeasible. Recently developed machine learning…