Related papers: Machine Learning model driven prediction of the in…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
We study the relation between elliptic flow, $v_2$ and the initial eccentricity, $\varepsilon_2$, in heavy-ion collisions, using hydrodynamic simulations. Significant deviations from linear eccentricity scaling are seen in more peripheral…
Machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computations. ML-based parameterizations within hybrid ESMs have…
The evolution of a relativistic heavy-ion collision is typically understood as a process that transmutes the initial geometry of the system into the final momentum distribution of observed hadrons, which can be described via a cumulant…
Few-shot learning is a promising approach to molecular property prediction as supervised data is often very limited. However, many important molecular properties depend on complex molecular characteristics -- such as the various 3D…
This work presents a Bayesian inference study for relativistic heavy-ion collisions in the Beam Energy Scan program at the Relativistic Heavy-Ion Collider. The theoretical model simulates event-by-event (3+1)D collision dynamics using…
Machine learning (ML) is widely used in drug discovery to train models that predict protein-ligand binding. These models are of great value to medicinal chemists, in particular if they provide case-specific insight into the physical…
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these…
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling…
We describe in detail how the different components of a multi-phase transport (AMPT) model, that uses the Heavy Ion Jet Interaction Generator (HIJING) for generating the initial conditions, Zhang's Parton Cascade (ZPC) for modeling partonic…
Relativistic heavy-ion collisions suggest that low momentum regions of the observed particle spectra are thermal and hydrodynamic, while medium-high momentum regions are non-thermal and perturbative. In this study, I construct a…
The goal of the ultra-relativistic heavy ion program is to study Quantum Chromodynamics under finite temperature and density conditions. After a couple of decades of experiment, the focus at the top RHIC and the LHC energy has evolved to…
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 prediction of atmospheric optical turbulence in localized environments is essential for estimating the performance of free-space optical systems. Macro-meteorological models developed to predict turbulent effects in one environment…
This research showcases the innovative integration of Large Language Models into machine learning workflows for traffic incident management, focusing on the classification of incident severity using accident reports. By leveraging features…
In the framework of the Glauber approach we analyze the shape parameters of the early-formed system and their event-by-event fluctuations. We test a variety of models: the conventional wounded nucleon model, a model admixing binary…
The success of hydrodynamics in high energy heavy-ion collisions leads to a flow paradigm, to understand the observed features of harmonic flow in terms of the medium collective expansion regarding initial state geometrical properties. In…
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for intelligent road inspection. The traditional…
Heavy-ion collisions at $\sqrt{s_\mathrm{NN}} \sim 10$ GeV probe the QCD phase diagram at large baryon densities. Because the longitudinal Lorentz contraction is small at these collision energies, understanding the dynamics during the early…
Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the…