Related papers: An End-to-End Generative Diffusion Model for Heavy…
The recently proposed participant dissipating effective-energy approach is applied to describe the dependence on centrality of the multiplicity of charged particles measured in heavy-ion collisions at the collision energies up to the…
Diffusion generative models unlock new possibilities for inverse problems as they allow for the incorporation of strong empirical priors in scientific inference. Recently, diffusion models are repurposed for solving inverse problems using…
The present research proposes a new memory-efficient method using diffusion models to inject turbulent inflow conditions into Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) for various flow problems. A guided diffusion…
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two novel methods that generate LHC jets as point clouds…
We study the dynamics of energy loss and flow of heavy quarks produced in ultra-relativistic heavy-ion collisions within the framework of a Langevin equation coupled to a (2+1)-dimensional viscous hydrodynamic model that simulates the…
By taking into account the effects of leading particles, we discuss the pseudorapidity distributions of the charged particles produced in high energy heavy ion collisions in the context of evolution-dominated hydrodynamic model. The leading…
We present a novel generative modeling framework,Wavelet-Fourier-Diffusion, which adapts the diffusion paradigm to hybrid frequency representations in order to synthesize high-quality, high-fidelity images with improved spatial…
Diffusion models offer stable training and state-of-the-art performance for deep generative modeling tasks. Here, we consider their use in the context of multivariate subsurface modeling and probabilistic inversion. We first demonstrate…
High-fidelity electron microscopy simulations required for quantitative crystal structure refinements face a fundamental challenge: while physical interactions are well-described theoretically, real-world experimental effects are…
Deep generative models have advanced text-to-online handwriting generation (TOHG), which aims to synthesize realistic pen trajectories conditioned on textual input and style references. However, most existing methods still primarily focus…
Data assimilation plays a crucial role in numerical modeling, enabling the integration of real-world observations into mathematical models to enhance the accuracy and predictive capabilities of simulations. This approach is widely applied…
The experimental data on hadron yields and ratios in central lead-lead and gold-gold collisions at 158 AGeV/$c$ (SPS) and $\sqrt{s} = 130$ AGeV (RHIC), respectively, are analysed within a two-source statistical model of an ideal hadron gas.…
We numerically analyze recent high energy heavy-ion collision experiments based on a hydrodynamical model with phase transition and discuss a systematic change of initial state of QGP-fluid depending on colliding-nuclei's mass. In a…
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theory modeling assumptions. Petabytes of simulated data are…
A selection of experimental results in high-energy nucleus-nucleus collisions after five years of operation of the Relativistic Heavy-Ion Collider (RHIC) is presented. Emphasis is put on measurements that provide direct information on…
It is a known problem that deep-learning-based end-to-end (E2E) channel coding systems depend on a known and differentiable channel model, due to the learning process and based on the gradient-descent optimization methods. This places the…
We present recent results on bulk observables and electromagnetic probes obtained using a hybrid approach based on the Ultrarelativistic Quantum Molecular Dynamics transport model with an intermediate hydrodynamic stage for the description…
Improving the quality of hyperspectral images (HSIs), such as through super-resolution, is a crucial research area. However, generative modeling for HSIs presents several challenges. Due to their high spectral dimensionality, HSIs are too…
We discuss a standard model of heavy ion collisions that has emerged both from the experimental results of the RHIC program and associated theoretical developments. We comment briefly on the impact of early results of the LHC program on…
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train…