Proceedings of the 35th IUPAP Conference on Computational Physics
CCP2024, July 7-12, Thessaloniki, Greece
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Beschreibung
This book comprises a selection of lectures presented at the IUPAP Conference on Computational Physics (CCP2024), held in July 2024 in Thessaloniki, Greece. The meeting highlighted recent research advances across a broad spectrum of physics, with particular emphasis on studies employing computational and simulation-based methodologies. The increasing accessibility of High-Performance Computing (HPC) resources has enabled researchers to address some of the most challenging problems in the field through optimized parallel programming frameworks, including MPI and OpenMP, as well as GPU-accelerated computing. Such approaches have become standard practice in many of the research topics represented in this book.
A prominent feature of the conference was the integration of emerging methodologies based on Artificial Intelligence (AI) and Machine Learning (ML). Contributions demonstrated that these techniques can significantly enhance computational efficiency while maintaining high levels of accuracy. Numerical simulations play a central role in many of the included works, particularly in studies of complex systems employing network theory, advanced HPC strategies, and AI-augmented computational frameworks, as presented by leading researchers in their respective areas.
This book is intended for researchers, practitioners, and graduate students across all areas of Physics, who seek to apply state-of-the-art computational techniques, numerical modeling, computer simulations, and data-driven methods. The individual chapters may also serve as instructional material for graduate-level courses in computational physics, numerical methods, and high-performance computing.
This book comprises a selection of lectures presented at the IUPAP Conference on Computational Physics (CCP2024), held in July 2024 in Thessaloniki, Greece. The meeting highlighted recent research advances across a broad spectrum of physics, with particular emphasis on studies employing computational and simulation-based methodologies. The increasing accessibility of High-Performance Computing (HPC) resources has enabled researchers to address some of the most challenging problems in the field through optimized parallel programming frameworks, including MPI and OpenMP, as well as GPU-accelerated computing. Such approaches have become standard practice in many of the research topics represented in this book.
A prominent feature of the conference was the integration of emerging methodologies based on Artificial Intelligence (AI) and Machine Learning (ML). Contributions demonstrated that these techniques can significantly enhance computational efficiency while maintaining high levels of accuracy. Numerical simulations play a central role in many of the included works, particularly in studies of complex systems employing network theory, advanced HPC strategies, and AI-augmented computational frameworks, as presented by leading researchers in their respective areas.
This book is intended for researchers, practitioners, and graduate students across all areas of Physics, who seek to apply state-of-the-art computational techniques, numerical modeling, computer simulations, and data-driven methods. The individual chapters may also serve as instructional material for graduate-level courses in computational physics, numerical methods, and high-performance computing.
Contains new techniques of MPI and OpenMP parallel programming optimizations including GPU acceleration in HPC Includes new innovative open-source software for a variety of applications Provides new machine learning (ML) and deep learning (DL) algorithms
Autor*in
Panos Argyrakis
Themen in »Proceedings of the 35th IUPAP Conference on Computational Physics«
Quantum Machine Learning Quantum Algorithms and Technology Green Computing Machine Learning and Data Science Applications in Physics Computational Electrodynamics Multi-scale and Multi-Physics Simulations Neuromorphic Computing Lattice Quantum Chromodynamics Lattice Gauge Theory Relativistic Numerical Astrophysics Mineral Physics Quantum Theory of Solids Atomic Electronic Structure in Materials Theory and Simulation of Nanoscale Phenomena Machine Learning on Gravitation and Cosmology