相关论文: Simultaneous Estimation of Elliptic Flow Coefficie…

Convolutional Neural Networks (CNN) have been a good solution for understanding a vast image dataset. As the increased number of battery-equipped electric vehicles is flourishing globally, there has been much research on understanding which…

We apply a quark combination model with equal-velocity combination (EVC) approximation to study the elliptic flow ($v_{2}$) of hadrons in heavy-ion collisions in a wide collision energy range ($\sqrt{s_{NN}}=$ 27 - 5020 GeV). Utilizing the…

This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of micro-CT images of real microgeometries. The primary goal is to develop an efficient,…

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling…

This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed…

The accurate prediction of airfoil pressure distribution is essential for aerodynamic performance evaluation, yet traditional methods such as computational fluid dynamics (CFD) and wind tunnel testing have certain bottlenecks. This paper…

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics to extract flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with…

The effect of final state interactions on the elliptic flow coefficient measured in relativistic heavy ion collisions is investigated within the DWEF formalism established by Miller and Cramer [Phys. Rev. Lett. 94, 102302 (2005), J. Phys.…

Machine learning techniques have been quite popular recently in the high-energy physics community and have led to numerous developments in this field. In heavy-ion collisions, one of the crucial observables, the impact parameter, plays an…

Improved constraints on current model parameters in a heavy-ion collision model are established using the latest measurements from three distinct collision systems. Various observables are utilized from Au--Au collisions at…

Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learning based radiomic studies build predictive models in a…

The correlation between the elliptic flow $v_2$ scaled by the impact parameter $b$ and the shear viscosity $\eta$ as well as the specific viscosity $\eta/s$, defined as the ratio of the shear viscosity to the entropy density $s$, is…

In this study, we explore the applicability of Transfer Learning techniques for estimating collision centrality in terms of the number of participants ($N_{\rm part}$) in high-energy heavy-ion collisions. In the present work, we leverage…

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…

We developed an efficient classifier that sorts alpha-decay events from various vertex-like objects in nuclear emulsion using a convolutional neural network (CNN). Alpha-decay events in the emulsion are standard calibration sources for the…

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…

Deep learning applications have achieved great success in numerous real-world applications. Deep learning models, especially Convolution Neural Networks (CNN) are often prototyped using FPGA because it offers high power efficiency and…

In the present work, 3D convolutional neural networks (CNNs) are trained to link random heterogeneous, two-phase materials of arbitrary phase fractions to their elastic macroscale stiffness thus replacing explicit homogenization…

We focus on a convolutional neural network (CNN), which has recently been utilized for fluid flow analyses, from the perspective on the influence of various operations inside it by considering some canonical regression problems with fluid…

The purpose of this work is to examine the application of a deep learning model in event reconstruction of neutrino interactions. The challenges faced in event reconstruction include the placement of an accurate primary neutrino interaction…