Related papers: Probing Proton Structure via Physics-Guided Neural…
Understanding the internal structure of the proton-including its mass spectrum, electromagnetic and gravitational form factors, and mechanical properties-remains a central challenge in hadronic physics. While lattice QCD and experimental…
Understanding the internal structure of the proton-including the distributions of quarks and gluons and their contributions to proton properties such as mass-remains a central challenge in quantum chromodynamics (QCD). While quark…
We study the proton structure functions $F_1$ and $F_2$ in the context of holography. We develop a general framework that extends previous holographic calculations of $F_1$ and $F_2$ to the case where the bulk geometry stems from bottom-up…
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein…
We present analyses on nucleon structure functions at the small Bjorken-$x$ in the framework of holographic QCD. In this study, we improve the description of the target nucleon in the current setup of the holographic model by introducing a…
We study the nucleon and pion structure functions at small Bjorken-$x$ region in the framework of holographic QCD with a special emphasis on the roles of AdS space wave functions. Using the BPST kernel for the Pomeron exchange and…
We employ Physics-Informed Neural Networks (PINNs) to solve the diffusion of heavy quarks within the expanding hot QCD medium generated in relativistic heavy-ion collisions. Due to the strong coupling between heavy quarks and the bulk…
We solve a long-standing problem in particle physics: that of deriving the Deep Inelastic structure functions of the proton from the fundamental theory of strong interactions, Quantum ChromoDynamics (QCD). In the Bjorken limit, the momenta…
We present a detailed analysis of the polarized and the unpolarized deep inelastic scattering structure functions of the proton, g_1^p and F_2^p respectively, in the context of a holographic dual description based on type IIB superstring…
Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and nonlinear friction. In this paper, these nonlinear dynamics…
Symmetric and antisymmetric structure functions from electromagnetic deep inelastic scattering of charged leptons off spin-1/2 hadrons are investigated in the framework of a top-down holographic dual description. We consider the BPST…
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDEs), particularly in a parametric setting. Among all…
Perturbative quantum chromodynamics (QCD) ceases to be applicable at low interaction energies due to the rapid increase of the strong coupling. In that limit, the non-perturbative regime determines the properties of quarks and gluons…
Graph neural networks (GNNs) naturally align with sparse operators and unstructured discretizations, making them a promising paradigm for physics-informed machine learning in computational mechanics. Motivated by discrete physics losses and…
Big-data-based artificial intelligence (AI) supports profound evolution in almost all of science and technology. However, modeling and forecasting multi-physical systems remain a challenge due to unavoidable data scarcity and noise.…
We employ the VQCD model, a holographic approach that dynamically simulates essential QCD characteristics, including linear mass spectra, confinement, asymptotic freedom, and magnetic charge screening, while incorporating quark flavor…
We study inclusive electroproduction on the proton at low $x$ and low $Q^2$ using a soft and a hard Pomeron. The contribution of the soft Pomeron is based on the Stochastic Vacuum Model, in which a nonperturbative dipole-dipole cross…
The proton diffractive structure function $F_2^{D(3)}$ measured in the H1 and ZEUS experiments at HERA is analyzed in terms of both Regge phenomenology and perturbative QCD evolution. A new method determines the values of the Regge…
This study demonstrates a proof-of-concept application of a deep neural network for particle identification in simulated high transverse momentum proton-proton collisions, with a focus on evaluating model performance under controlled…
Deep learning has achieved great success in modeling dynamical systems, providing data-driven simulators to predict complex phenomena, even without known governing equations. However, existing models have two major limitations: their narrow…