The Journal of Computational Physics (JCP) focuses on the computational aspects of physical problems. JCP encourages original scientific contributions in advanced mathematical and numerical modeling reflecting a combination of concepts, methods and principles which are often interdisciplinary in nature and span several areas of physics, mechanics, applied mathematics, statistics, applied geometry, computer science, chemistry and other scientific disciplines as well: the Journal's editors seek to emphasize methods that cross disciplinary boundaries.

JCP also encourages the submission of papers that develop innovative methods bridging mathematical, physical modeling and algorithmization, e.g. at the frontier between predictive simulation and machine learning. When addressing problems previously covered by other approaches, a comparison should be provided. As for any paper in JCP, the efficacy, robustness, computational complexity, as well as reproducibility should be addressed.

JCP also publishes short notes of 4 pages or less (including figures, tables, and references but excluding title pages). Letters to the Editor commenting on articles already published in this Journal will also be considered. Neither notes nor letters should have an abstract. Review articles providing a survey of particular fields are particularly encouraged. JCP does not impose a formal page limit. Authors are expected to present their work clearly and concisely. The handling Editor will assess whether the length of the manuscript is appropriate for the content and may request a shorter version of the submission if necessary. Published conference papers are welcome provided the submitted manuscript is a significant enhancement of the conference paper with substantial additions.

Reproducibility, that is the ability to reproduce results obtained by others, is a core principle of the scientific method. As the impact of and knowledge discovery enabled by computational science and engineering continues to increase, it is imperative that reproducibility becomes a natural part of these activities. The journal strongly encourages authors to make available all software or data that would allow published results to be reproduced and that every effort is made to include sufficient information in manuscripts to enable this. This should not only include information used for setup but also details on post-processing to recover published results.

Special Issue on Advances in Computational Energy Science: Science of AI in Energy and Energy-Stable Computational Science
提出日: 2026-07-15

The global energy transition demands unprecedented advancements in modeling, optimization, and control of complex energy systems. Computational science bridges theoretical research and real-world applications, with AI-driven methods and energy-stable computational techniques emerging as pivotal tools. This Special Issue focuses on the synergy between artificial intelligence and robust computational frameworks to address challenges in energy generation, transportation, storage, grid management, and policy design. Significant advances have been witnessed in this area, but accurate modeling and energy-stable computation remain challenging topics in many ways due to the high complexity of energy systems. AI and energy-stable computation are independently transforming energy science, yet their convergence remains under-explored in top journals. Interdisciplinarity is critical for tackling decarbonization, grid resilience, and energy justice, all requiring fused expertise from computational mathematics, computer science, and energy engineering. This Special Issue addresses the urgent need for advanced computational frameworks to accelerate the global energy transition, focusing on the synergistic integration of AI and Energy-Stable Computational Science. Scope highlights include AI-driven innovations, including physics-informed machine learning for energy system digital twins, generative AI for materials discovery, and reinforcement learning for grid optimization, alongside robust computational methods like structure-preserving algorithms, entropy-stable discretizations, and scalable HPC solvers for multiphysics energy challenges.

Guest editors:

Prof. Shuyu Sun
School of Mathematical Science, Tongji University, Shanghai, China

Prof. Huangxin Chen
School of Mathematical Science, Xiamen University, Xiamen, China

Prof. Tao Zhang
China University of Petroleum East China - Qingdao Campus, Qingdao, China

Prof. Ahmed H. Elsheikh
Heriot-Watt University, Edinburgh, United Kingdom

Prof. Dunhui Xiao
School of Mathematical Science, Tongji University, Shanghai, China

Manuscript submission information:

Manuscript submission deadline: 15-Jun-2026

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Prof. Shuyu Sun via sunupc@qq.com.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: COMPES-2025” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Energy transition, AI in energy systems, Physics-informed machine learning, Structure-preserving methods, High-performance computing (HPC)