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In this paper, we presented our recently developed DREENA-C framework, which is a fully optimized computational suppression procedure based on our state-of-the-art dynamical energy loss formalism in constant temperature finite size QCD…

Nuclear Theory · Physics 2018-05-10 Dusan Zigic , Igor Salom , Jussi Auvinen , Marko Djordjevic , Magdalena Djordjevic

Understanding properties of Quark-Gluon Plasma requires an unbiased comparison of experimental data with theoretical predictions. To that end, we developed the dynamical energy loss formalism which, in distinction to most other methods,…

We present a fully optimised framework DREENA-A based on a state-of-the-art energy loss model. The framework can include any, in principle arbitrary, temperature profile within the dynamical energy loss formalism. Thus, 'DREENA' stands for…

Nuclear Theory · Physics 2022-12-01 Dusan Zigic , Igor Salom , Jussi Auvinen , Pasi Huovinen , Magdalena Djordjevic

We explore to what extent, and how, high-$p_\perp$ data and predictions reflect the shape and anisotropy of the QCD medium formed in ultrarelativistic heavy-ion collisions. To this end, we use our recently developed DREENA-A framework,…

Nuclear Theory · Physics 2022-10-26 Stefan Stojku , Jussi Auvinen , Lidija Zivkovic , Pasi Huovinen , Magdalena Djordjevic

We show that high-$p_T$ $R_{AA}$ and $v_2$ are sensitive to the early expansion dynamics, and that the high-$p_T$ observables prefer delayed onset of energy loss and transverse expansion. To calculate high-$p_T$ $R_{AA}$ and $v_2$, we…

Nuclear Theory · Physics 2022-03-03 Stefan Stojku , Jussi Auvinen , Marko Djordjevic , Pasi Huovinen , Magdalena Djordjevic

We present a theoretical formalism for calculating first-order-in-opacity radiative energy loss that incorporates the spatial and temporal temperature evolution of the quark-gluon plasma (QGP) in a finite-size QCD medium with dynamical…

Nuclear Theory · Physics 2025-12-30 Bithika Karmakar , Magdalena Djordjevic

The scarce knowledge of the initial stages of quark-gluon plasma before the thermalization is mostly inferred through the low-$p_\perp$ sector. We propose a complementary approach in this report - the use of high-$p_\perp$ probes' energy…

High Energy Physics - Phenomenology · Physics 2022-03-23 Bojana Ilic , Dusan Zigic , Marko Djordjevic , Magdalena Djordjevic

RNA function is tied to secondary structure, operating through dynamic and heterogeneous structural ensembles. While current analysis tools typically output single static structures or averaged contact maps, chemical probing methods like…

Biomolecules · Quantitative Biology 2026-05-20 Giuseppe Sacco , Jianhui Li , Redmond P. Smyth , Guido Sanguinetti , Giovanni Bussi

We provide a concise review on recent theory advancements towards full-fledged (3+1)D dynamical descriptions of relativistic nuclear collisions at finite baryon density. Heavy-ion collisions at different collision energies produce…

Nuclear Theory · Physics 2021-10-27 Shanjin Wu , Chun Shen , Huichao Song

We study the production and evolution of charm and bottom quarks in hot partonic medium produced in heavy ion collisions. The heavy quarks loose energy in the medium which is reflected in the transverse momentum spectra of heavy mesons. The…

High Energy Physics - Phenomenology · Physics 2017-04-05 Kapil Saraswat , Prashant Shukla , Vineet Kumar , Venktesh Singh

Jet suppression is considered to be an excellent probe of QCD matter created in ultra-relativistic heavy ion collisions. Our theoretical predictions of jet suppression, which are based on our recently developed dynamical energy loss…

Nuclear Theory · Physics 2015-05-05 Bojana Blagojevic , Magdalena Djordjevic

Relativistic hydrodynamics is a powerful tool to simulate the evolution of the quark gluon plasma (QGP) in relativistic heavy ion collisions. Using 10000 initial and final profiles generated from 2+1-d relativistic hydrodynamics VISH2+1…

Nuclear Theory · Physics 2021-07-07 Hengfeng Huang , Bowen Xiao , Ziming Liu , Zeming Wu , Yadong Mu , Huichao Song

In many supervised learning applications, the response consists of both continuous and binary outcomes. Studies have shown that jointly modeling such mixed-type responses can substantially improve predictive performance compared to separate…

Methodology · Statistics 2026-03-13 Yu Wang , Ran Jin , Lulu Kang

Finite Element Analysis (FEA) is a powerful but computationally intensive method for simulating physical phenomena. Recent advancements in machine learning have led to surrogate models capable of accelerating FEA. Yet there are still…

Machine Learning · Computer Science 2025-02-18 Georgios Triantafyllou , Panagiotis G. Kalozoumis , George Dimas , Dimitris K. Iakovidis

This paper extends the finite element network analysis (FENA) to include a dynamic time-transient formulation. FENA was initially formulated in the context of the linear static analysis of 1D and 2D elastic structures. By introducing the…

Computational Engineering, Finance, and Science · Computer Science 2024-07-04 Mehdi Jokar , Siddharth Nair , Fabio Semperlotti

Inspired by recent progress in dynamic programming approaches for weighted model counting, we investigate a dynamic-programming approach in the context of boolean realizability and synthesis, which takes a conjunctive-normal-form boolean…

Formal Languages and Automata Theory · Computer Science 2024-06-21 Yi Lin , Lucas M. Tabajara , Moshe Y. Vardi

Identifying dynamical system (DS) is a vital task in science and engineering. Traditional methods require numerous calls to the DS solver, rendering likelihood-based or least-squares inference frameworks impractical. For efficient parameter…

Computation · Statistics 2024-09-19 Ying Zhou , Jinglai Li , Xiang Zhou , Hongqiao Wang

High penetration of renewables (RES) and the retirement of thermal units aggravate flexibility scarcity in power systems. Hydrogen-based low-carbon steel production systems possess substantial demand response (DR) potential. This paper…

Systems and Control · Electrical Eng. & Systems 2026-05-04 Qiang Ji , Lin Cheng , Yue Zhou , Ning Qi , Kaidi Huang , Jianzhong Wu , Ming Cheng

We introduce a generative learning framework to model high-dimensional parametric systems using gradient guidance and virtual observations. We consider systems described by Partial Differential Equations (PDEs) discretized with structured…

Machine Learning · Computer Science 2024-08-02 Han Gao , Sebastian Kaltenbach , Petros Koumoutsakos

Interactions of high momentum partons with Quark-Gluon Plasma created in relativistic heavy-ion collisions provide an excellent tomography tool for this new form of matter. Recent measurements for charged hadrons and unidentified jets at…

Nuclear Theory · Physics 2017-11-08 Magdalena Djordjevic
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