An International Journal and Program Library for Computational Physics

Visit the International Computer Program Library on Mendeley Data.

Computer Physics Communications publishes research papers and application software in the broad field of computational physics; current areas of particular interest are reflected by the research interests and expertise of the CPC Editorial Board.

The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper.

    Computer Programs in Physics (CPiP)
    These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged.

    Computational Physics Papers (CP)
    These are research papers in, but are not limited to, the following themes across computational physics and related disciplines.

        mathematical and numerical methods and algorithms;

        computational models including those associated with the design, control and analysis of experiments; and

        algebraic computation. Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository. In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.

The introduction to each paper should be directed to a general audience and the author(s) must clearly articulate the novelty and significance of the paper and how it will advance the solution of an important physics application. Papers which, in the opinion of a Principal Editor, fail to do this will not be sent for review. The editor may consult with experts in the field in making this decision.

Special Issue on Advances in physics aware machine learning
Submission Date: 2024-10-01

Traditional machine learning methods often focus solely on data patterns and correlations without explicitly incorporating the underlying physics. While these approaches can be effective for tasks like image recognition or language processing, they may struggle to capture the intricate dynamics and complex interactions present in physics systems. 

Guest editors:

Dr Sibo Cheng (Executive Guest Editor)
Imperial college London, London, UK
Sibo.cheng@imperial.ac.uk

Dr Rossella Arcucci
Imperial college London, London, UK
r.arcucci@imperial.ac.uk

Dr Didier Lucor
LISN, Paris-Saclay university, Palaiseau, France
didier.lucor@lisn.upsaclay.fr

Dr Jinlong Fu
Queen Mary University, London, UK
jinlong.fu@qmul.ac.uk

Professor Weiping Ding
Nantong University, Nantong, China
ding.wp@ntu.edu.cn

Professor Yike Guo
The Hong Kong University of Science and Technology
yikeguo@ust.hk

Special issue information:

Traditional machine learning methods often focus solely on data patterns and correlations without explicitly incorporating the underlying physics. While these approaches can be effective for tasks like image recognition or language processing, they may struggle to capture the intricate dynamics and complex interactions present in physics systems. Physics-aware machine learning integrates domain-specific knowledge into machine learning algorithms, enabling more accurate and interpretable solutions. Also, the physics knowledge can contribute to training machine learning when available data is limited or sparse. By bridging the gap between physics and machine learning, we can unlock the full potential of data driven approaches while ensuring their compatibility with the underlying physical principles. Numerous ongoing challenges persist in the field of physicsinformed machine learning, including multi-scale modelling, the mitigation of training expenses through complex physical loss functions, handling unstructured data such as adaptive meshes, and addressing uncertainty quantification and propagation. This special issue aims to bring together the latest advancements in physics-aware machine learning and its novel applications to diverse engineering disciplines, by welcoming both theoretical and computational works related (but not limited) to:

• Machine learning-based reduced order modelling techniques for high-dimensional systems
• Methodology development for Physics-informed neural networks (PINNs)
• Theory or novel applications of machine learning algorithms with data assimilation for highdimensional physics systems
• Physics aware uncertainty quantification and sensitivity analysis for data-driven models
• Machine learning in the spectral domain for complex physics problems
• Parameter estimation for complex physics systems using machine learning techniques • Cutting-edge Computer Programs related to physics-aware machine learning or data science covered by an approved open source licence
• High-quality review papers, related to the aforementioned topics

Manuscript submission information:

The journal’s submission platform (Editorial Manager®) is now available for receiving submissions to this Special Issue. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: physics machine learning” when submitting your manuscript online. 

Both the Guide for Authors and the submission portal could be found on the Journal Homepage here: Guide for authors - Computer Physics Communications - ISSN 0010-4655 | ScienceDirect.com by Elsevier
Timeline:

    Final Manuscript Submission Deadline:  01/10/2024
    Editorial Acceptance Deadline: 01/02/2025

Keywords:

Scientific machine learning; Physics aware machine learning; uncertainty quantification; Dynamical systems; Open access software