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We conducted three-dimensional lattice simulations to study the density perturbation and gravitational waves (GWs) during first-order phase transition (FOPT). We find that for phase transition strength $\alpha > 1$, the forward motion of…

High Energy Physics - Phenomenology · Physics 2026-04-13 Jintao Zou , Zhiqing Zhu , Zizhuo Zhao , Ligong Bian

Circumstellar discs, from which planetary systems are formed, consist of gas, dust and solids. Simulations of self-consistent dynamics of gas, dust and solids in circumstellar discs is a challenging problem. In the paper we present fast…

Using the holographic correspondence as a tool, we determine the steady-state velocity of expanding vacuum bubbles nucleated within chiral finite temperature first-order phase transitions occurring in strongly-coupled large $N$ QCD-like…

High Energy Physics - Phenomenology · Physics 2021-10-06 Francesco Bigazzi , Alessio Caddeo , Tommaso Canneti , Aldo L. Cotrone

Normalizing flows are a class of probabilistic generative models which allow for both fast density computation and efficient sampling and are effective at modelling complex distributions like images. A drawback among current methods is…

Computer Vision and Pattern Recognition · Computer Science 2020-10-28 Jason J. Yu , Konstantinos G. Derpanis , Marcus A. Brubaker

We study the dynamics of cosmological phase transitions in the case of small velocities of bubble walls, $v_w<0.1$. We discuss the conditions in which this scenario arises in a physical model, and we compute the development of the phase…

Cosmology and Nongalactic Astrophysics · Physics 2018-03-14 Ariel Megevand , Santiago Ramirez

This study theoretically considers the motion of N identical inelastic particles between two oscillating walls. The particles' average energy increases abruptly at certain critical filling fractions, wherein the system changes into a…

Data Analysis, Statistics and Probability · Physics 2011-01-31 Fei Fang Chung , Sy-Sang Liaw , Wei Chun Chang

In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art algorithms for analyzing non-euclidean graph data. By applying deep-learning to extract high-level representations from graph structures, GNNs achieve extraordinary…

Artificial Intelligence · Computer Science 2021-06-28 Zhe Zhou , Bizhao Shi , Zhe Zhang , Yijin Guan , Guangyu Sun , Guojie Luo

Partial wave analysis is a key technique in hadron spectroscopy. The use of unbinned likelihood fits on large statistics data samples and ever more complex physics models makes this analysis technique computationally very expensive.…

Data Analysis, Statistics and Probability · Physics 2011-08-31 Niklaus Berger

In this work we present a framework to explain the prediction of the velocity fluctuation at a certain wall-normal distance from wall measurements with a deep-learning model. For this purpose, we apply the deep-SHAP method to explain the…

Fluid Dynamics · Physics 2025-02-12 A. Cremades , R. Freibergs , S. Hoyas , A. Ianiro , S. Discetti , R. Vinuesa

Numerical simulations of flows are required for numerous applications, and are usually carried out using shallow water equations. We describe the FullSWOF software which is based on up-to-date finite volume methods and well-balanced schemes…

Fusion simulations have traditionally required the use of leadership scale High Performance Computing (HPC) resources in order to produce advances in physics. The impressive improvements in compute and memory capacity of many-GPU compute…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-07-12 Emily A. Belli , Jeff Candy , Igor Sfiligoi , Frank Würthwein

Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology, and human health. As a direct measure of the shear-induced…

Fluid Dynamics · Physics 2023-10-19 Esther Lagemann , Steven L. Brunton , Christian Lagemann

GAMMA_FLOW is an open-source Python package for real-time analysis of spectral data. It supports classification, denoising, decomposition, and outlier detection of both single- and multi-component spectra. Instead of relying on large,…

Machine Learning · Computer Science 2025-11-13 Viola Rädle , Tilman Hartwig , Benjamin Oesen , Emily Alice Kröger , Julius Vogt , Eike Gericke , Martin Baron

We present a new method to estimate terminal bubble velocities during first-order phase transitions in a plasma in local equilibrium. The method relies on calculating the extrema of a modified potential function for the scalar field…

High Energy Physics - Phenomenology · Physics 2025-12-01 Martin Münzenberg , Carlos Tamarit

Functional integral representations for solutions of the motion equations for wall-bounded incompressible viscous flows, expressed (implicitly) in terms of distributions of solutions to stochastic differential equations of McKean-Vlasov…

Numerical Analysis · Mathematics 2024-03-26 Vladislav Cherepanov , Sebastian W. Ertel , Zhongmin Qian , Jiang-Lun Wu

The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are performed by training a fully-convolutional network (FCN) to…

Quantum solutions to differential equations represent quantum data -- states that contain relevant information about the system's behavior, yet are difficult to analyze. We propose a toolbox for reading out information from such data, where…

The velocity circulation, a measure of the rotation of a fluid within a closed path, is a fundamental observable in classical and quantum flows. It is indeed a Lagrangian invariant in inviscid classical fluids. In quantum flows, circulation…

Fluid Dynamics · Physics 2021-03-17 Nicolás P. Müller , Juan Ignacio Polanco , Giorgio Krstulovic

The cosmological first-order phase transition (FOPT) can be of strong dynamics but with its bubble wall velocity difficult to be determined due to lack of detailed collision terms. Recent holographic numerical simulations of strongly…

High Energy Physics - Theory · Physics 2023-11-30 Li Li , Shao-Jiang Wang , Zi-Yan Yuwen

We introduce CaLES, a GPU-accelerated finite-difference solver designed for large-eddy simulations (LES) of incompressible wall-bounded flows in massively parallel environments. Built upon the existing direct numerical simulation (DNS)…

Fluid Dynamics · Physics 2024-11-18 Maochao Xiao , Alessandro Ceci , Pedro Costa , Johan Larsson , Sergio Pirozzoli