Related papers: Estimating Elliptic Flow Coefficient in Heavy Ion …

Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton's…

I compare the first viscous hydrodynamic prediction for integrated elliptic flow in Pb-Pb collisions at the LHC with the first data released by the ALICE collaboration. These new data are found to be consistent with hydrodynamic…

In this article, we briefly review recent progress on hydrodynamic modeling and its implementations to relativistic heavy-ion collisions at RHIC and the LHC. The related topics include: 1) initial state fluctuations, final state…

The strong fluctuations in the initial energy density of heavy-ion collisions allow an efficient selection of events corresponding to a specific initial geometry. For such "shape engineered events", the elliptic flow coefficient, $v_2$, of…

Recently it has been discovered that the elliptic flow, v2, of composite charged particles emitted at midrapidity in Heavy-Ion collisions at intermediate energies shows the strongest sensitivity to the Nuclear Equation of State (EoS) which…

Most power systems' approaches are currently tending towards stochastic and probabilistic methods due to the high variability of renewable sources and the stochastic nature of loads. Conventional power flow (PF) approaches such as…

I review the recent progress in measuring elliptic flow in heavy ion collisions. These measurements show clearly how hydrodynamics starts to develop as the system size is increased from peripheral to central collisions. During this…

Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing…

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered…

Elliptic flow (v_2) values for identified particles at midrapidity in Au + Au collisions measured by the STAR experiment in the Beam Energy Scan at the Relativistic Heavy Ion Collider at sqrt{s_{NN}}= 7.7--62.4 GeV are presented for three…

Fast and accurate treatment of collisions in the context of modern N-body planet formation simulations remains a challenging task due to inherently complex collision processes. We aim to tackle this problem with machine learning (ML), in…

Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents. Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents.…

We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine-learning models are developed; namely the convolutional neural…

We apply the parton recombination approach to study the energy dependence of the elliptic flow, v_2 in heavy ion collisions from AGS to LHC energies. The relevant input quantities ($T, \mu_B, \eta_T$) at the various center of mass energies…

We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level…

Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real…

Deep learning for distribution grid optimization can be advocated as a promising solution for near-optimal yet timely inverter dispatch. The principle is to train a deep neural network (DNN) to predict the solutions of an optimal power flow…

In this work, we use ML techniques to develop presumed PDF models for large eddy simulations of reacting flows. The joint sub-filter PDF of mixture fraction and progress variable is modeled using various ML algorithms and commonly used…

Using a dynamical model based on the $NN \to d\pi$, $NNN \to dN$, and $NN\pi \to d\pi$ reactions and measured proton and pion transverse momentum spectra and elliptic flows, we study the production of deuterons and their elliptic flow in…

Recent endeavors aimed at forecasting future traffic flow states through deep learning encounter various challenges and yield diverse outcomes. A notable obstacle arises from the substantial data requirements of deep learning models, a…