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arXiv 2024年

作者： Derlatka, Kacper Manna, Maciej Bulenok, Oleksii Zwicker, David Arabas, Sylwester Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Faculty of Physics and Applied Computer Science AGH Univeristy of Krakow Poland

The numba-mpi package offers access to the Message Passing Interface (MPI) routines from Python code that uses the Numba just-in-time (JIT) compiler. As a result, high-performance and multi-threaded Python code may utilize MPI communication facilities without leaving the JIT-compiled code blocks, which is not possible with the mpi4py package, a higher-level Python interface to MPI. For debugging purposes, numba-mpi retains full functionality of the code even if the JIT compilation is disabled. The numba-mpi API constitutes a thin wrapper around the C API of MPI and is built around Numpy arrays including handling of non-contiguous views over array slices. Project development is hosted at GitHub leveraging the mpi4py/setup-mpi workflow enabling continuous integration tests on Linux (MPICH, OpenMPI & Intel MPI), macOS (MPICH & OpenMPI) and Windows (MS MPI). The paper covers an overview of the package features, architecture and performance. As of v1.0, the following MPI routines are exposed and covered by unit tests: size/rank, [i]send/[i]recv, wait[all|any], test[all|any], allreduce, bcast, barrier, scatter/[all]gather & wtime. The package is implemented in pure Python and depends on numpy, numba and mpi4py (the latter used at initialization and as a source of utility routines only). The performance advantage of using numba-mpi compared to mpi4py is depicted with a simple example, with entirety of the code included in listings discussed in the text. Application of numba-mpi for handling domain decomposition in numerical solvers for partial differential equations is presented using two external packages that depend on numba-mpi: py-pde and PyMPDATA-MPI. Copyright © 2024, The Authors. All rights reserved.

关键词： Python

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Can we observe effects of early nonequilibrium dynamics using collective flow

arXiv

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arXiv 2023年

作者： Bozek, Piotr AGH University of Krakow Faculty of Physics and Applied Computer Science aleja Mickiewicza 30 Cracow30-059 Poland

The rapid expansion of the fireball created in a heavy-ion collision causes strong departures from local equilibrium. Such effects are especially important in the very early phase of the collision, bringing a substantial pressure asymmetry. We investigate effects of this early stage pressure asymmetry in a kinetic model without boost-invariance. In the kinetic evolution the pressure asymmetry results from the competition of the longitudinal expansion and the kinetic relaxation. Unlike the transverse flow, the directed flow is found to be very sensitive to the equilibration rate and the pressure anisotropy. © 2023, CC BY.

关键词： Heavy ions

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High energy bremsstrahlung at the FCC-ee, FCC-eh and LHeC

arXiv

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arXiv 2023年

作者： Piotrzkowski, Krzysztof Przybycien, Mariusz AGH University of Science and Technology Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 Krakow30-055 Poland

Bremsstrahlung spectra will be strongly distorted due to small lateral beam sizes at future colliders. That in turn will have large consequences for the electron and positron beam lifetimes as well as for the luminosity measurements in the case of electron-hadron colliders. We discuss in detail such consequences for the Future Circular Collider and Large Hadron electron Collider cases. © 2023, CC BY.

关键词： Hadrons

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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physics Informed Neural Networks with strong and weak residuals for advection-dominated diffusion problems

arXiv

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arXiv 2023年

作者： Sikora, Maciej Krukowski, Patryk Paszyńska, Anna Paszyński, Maciej Institute of Computer Science AGH University of Science and Technology Kraków Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków Poland

This paper deals with the following important research questions. Is it possible to solve challenging advection-dominated diffusion problems in one and two dimensions using physics Informed Neural Networks (PINN) and Variational physics Informed Neural Networks (VPINN)? How does it compare to the higher-order and continuity Finite Element Method (FEM)? How to define the loss functions for PINN and VPINN so they converge to the correct solutions? How to select points or test functions for training of PINN and VPINN? We focus on the one-dimensional advection-dominated diffusion problem and the two-dimensional Eriksson-Johnson model problem. We show that the standard Galerkin method for FEM cannot solve this problem. We discuss the stabilization of the advection-dominated diffusion problem with the Petrov-Galerkin (PG) formulation and present the FEM solution obtained with the PG method. We employ PINN and VPINN methods, defining several strong and weak loss functions. We compare the training and solutions of PINN and VPINN methods with higher-order FEM methods. © 2023, CC BY.

关键词： Advection

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Factorial cumulants from short-range correlations and global baryon number conservation

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Physical Review C 2022年第2期106卷 024904-024904页

作者： Michał Barej Adam Bzdak AGH University of Science and Technology Faculty of Physics and Applied Computer Science 30-059 Kraków Poland

We calculate the baryon factorial cumulants assuming arbitrary short-range correlations and the global baryon number conservation. The general factorial cumulant generating function is derived. Various relations between factorial cumulants subjected to baryon number conservation and the factorial cumulants without this constraint are presented. We observe that for nth factorial cumulant the short-range correlations of more than n particles are suppressed with increasing number of particles. The recently published [V. Vovchenko et al., Phys. Lett. B 811, 135868 (2020)] relations between the cumulants in a finite acceptance with global baryon conservation and the grand-canonical susceptibilities are reproduced.

关键词： Particle correlations & fluctuations Relativistic heavy-ion collisions

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Graph-Grammar Based Longest-Edge Refinement Algorithm for Three-Dimensional Optimally p Refined Meshes with Tetrahedral Elements 21st

Graph-Grammar Based Longest-Edge Refinement Algorithm for Th...

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21st International Conference on Computational science, ICCS 2021

作者： Mosialek, Albert Szaflarski, Andrzej Pych, Rafal Kisiel-Dorohinicki, Marek Paszyński, Maciej Paszyńska, Anna Institute of Computer Science AGH University of Science and Technology Krakow Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland

ISBN: (纸本)9783030779696

Finite element method is a popular way of solving engineering problems in geoengineering. Three-dimensional grids employed for approximation the formation layers are often constructed from tetrahedral finite elements. The refinement algorithms that avoids hanging nodes are desired in order to avoid constrained approximation on broken edges and faces. We present a new mesh refinement algorithm for such the tetrahedral grids, with the following features (1) it is a two-level algorithm, refining the elements’ faces first, followed by the refinement of the elements’ interiors;(2) for the face refinements it employs the graph-grammar based version of the longest-edge refinement algorithm to avoid the hanging nodes;and (3) it allows for nearly perfect parallel execution of the second stage, refining the element interiors. We describe the algorithm using the graph-grammar based formalism. We verify the properties of the algorithm, by breaking 5,000 tetrahedral elements, and checking their angles and proportions. On the generated meshes without hanging nodes we span the polynomial basis functions of the optimal order, selected via metaheuristic optimization algorithm. We use them for the projection based interpolation of formation layers. © 2021, Springer Nature Switzerland AG.

关键词： Optimization

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Lévy flights and Lévy walks under stochastic resetting

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Physical Review E 2024年第4期109卷 044147-044147页

作者： Bartosz Żbik Bartłomiej Dybiec Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Łojasiewicza 11 30-348 Kraków Poland Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Łojasiewicza 11 30-348 Kraków Poland

Stochastic resetting is a protocol of starting anew, which can be used to facilitate the escape kinetics. We demonstrate that restarting can accelerate the escape kinetics from a finite interval restricted by two absorbing boundaries also in the presence of heavy-tailed, Lévy-type, α-stable noise. However, the width of the domain where resetting is beneficial depends on the value of the stability index α determining the power-law decay of the jump length distribution. For heavier (smaller α) distributions, the domain becomes narrower in comparison to lighter tails. Additionally, we explore connections between Lévy flights (LFs) and Lévy walks (LWs) in the presence of stochastic resetting. First of all, we show that for Lévy walks, the stochastic resetting can also be beneficial in the domain where the coefficient of variation is smaller than 1. Moreover, we demonstrate that in the domain where LWs are characterized by a finite mean jump duration (length), with the increasing width of the interval, the LWs start to share similarities with LFs under stochastic resetting.

关键词： Brownian motion Escape problems Levy flights Noise

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Improving Group Lasso for High-Dimensional Categorical Data 23rd

Improving Group Lasso for High-Dimensional Categorical Data

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23rd International Conference on Computational science, ICCS 2023

作者： Nowakowski, Szymon Pokarowski, Piotr Rejchel, Wojciech Soltys, Agnieszka Institute of Applied Mathematics and Mechanics University of Warsaw Warsaw Poland Faculty of Physics University of Warsaw Warsaw Poland Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruń Poland

ISBN: (纸本)9783031360206

Sparse modeling or model selection with categorical data is challenging even for a moderate number of variables, because roughly one parameter is needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection of continuous or categorical variables, but all estimates related to a selected factor usually differ. Therefore, a fitted model may not be sparse, which makes the model interpretation difficult. To obtain a sparse solution of the Group Lasso, we propose the following two-step procedure: first, we reduce data dimensionality using the Group Lasso;then, to choose the final model, we use an information criterion on a small family of models prepared by clustering levels of individual factors. In the consequence, our procedure reduces dimensionality of the Group Lasso and strongly improves interpretability of the final model. What is important, this reduction results only in the small increase of the prediction error. In the paper we investigate selection correctness of the algorithm in a sparse high-dimensional scenario. We also test our method on synthetic as well as the real data sets and show that it outperforms the state of the art algorithms with respect to the prediction accuracy, model dimension and execution time. Our procedure is contained in the R package DMRnet and available in the CRAN repository. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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