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 Numerical Methods for Engineering Quantum Computers
Día de Entrega: 2026-02-28

Numerical Algorithms for the optimal control of Qubit(s), computing ground states and sensitivity thereof due to perturbations in the Hamiltonian, Quantum or hybrid classical/quantum algorithms for benchmarking quantum computers, Quantum or hybrid Quantum/classical numerical methods for the unsteady/steady many body Schrodinger equation, Error Correction Algorithms, Computational Quantum Electrodynamics.

Guest editors:

Mark Sussman, PhD
Florida State University, Tallahassee, Florida, United States
(Multiphase Flows, Deforming boundary problems, Adaptive Mesh Refinement, Transport processes on Fluidic Interfaces, High performance Computing, Materials Processing, Computational Quantum Physics)
Email: sussman@math.fsu.edu

Stefanie Guenther, PhD
Lawrence Livermore National Laboratory, Livermore, California, United States
(optimization, HPC, quantum computing, quantum control, optimal control)
Email: guenther5@llnl.gov

Lin Lin, PhD
University of California Berkeley Department of Mathematics, Berkeley, California, United States
(Electronic structure theory, Quantum many-body physics, Quantum computation)
Email: linlin@math.berkeley.edu

Anders Petersson, PhD
Lawrence Livermore National Laboratory, Livermore, California, United States
(Numerical Analysis,Optimization,Wave Propagation,Quantum Control)
Email: petersson1@llnl.gov

Shi Jin, PhD
Shanghai Jiao Tong University - Fahua Campus, Shanghai, China
(Kinetic equations, hyperbolic conservation laws, quantum dynamics, high frequency waves, uncertainty quantification)
Email: shijin-m@sjtu.edu.cn

Philip Lotshaw, PhD
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
(Quantum computing,quantum simulation,optimization,computational physics,quantum information)
Email: lotshawpc@ornl.gov

Manuscript submission information:

Submission deadline: 28-Feb-2026

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: Numerical Methods for Engineering Quantum Computers" when submitting your manuscript online.

Authors are requested to submit their manuscripts electronically via the journal's online submission system. All submissions will undergo a rigorous peer-review process to ensure high quality and relevance of the published articles.Both the Guide for Authors and the submission portal could be found on the Journal Homepage here: Journal of Computational Physics | ScienceDirect.com by Elsevier

Keywords:

Computational Quantum Physics, Quantum Computer, control of Quantum Systems, Schrodinger equation, inverse problems in Quantum Mechanics, Quantum error correction, Hybrid Quantum/Classical algorithms, Benchmarking Quantum circuits, Computational Quantum Electrodynamics

Special Issue on Computational Methods in Additive Manufacturing
Día de Entrega: 2026-03-30

Additive manufacturing has the potential to vastly accelerate design innovation, compress supply chains and minimize material waste and energy usage. Full scale deployment of AM for advanced security and safety-critical energy applications will require a large step forward in our ability to predict printed material structure and properties and how they evolve from the complex multi-physics, multi-length scale build process, which is controlled by an overwhelmingly large number of parameters. This special issue is focused on the development and application of state-of-the-art computational methods to enable simulations of additive manufacturing processes.

Guest editors:

Prof. Frederic Gibou
University of California Santa Barbara, Santa Barbara, California, United States of America

Dr. Irene Beyerlein
University of California Santa Barbara, Santa Barbara, California, United States of America

Dr. Alain Karma
Northwestern University, Evanston, Illinois, United States of America

Special issue information:

Additive manufacturing (AM) has the transformative potential to dramatically accelerate design innovation, streamline and localize supply chains, and significantly reduce material waste and energy consumption. These advantages position AM as a foundational technology for next-generation engineering solutions, particularly in domains that demand agility, customization, and sustainability. However, the full-scale deployment of AM in advanced security and safety-critical energy systems—such as those found in aerospace, nuclear, and defense applications—hinges on a critical capability: the ability to reliably predict the structure and properties of printed materials, both immediately post-fabrication and as they evolve in service.

Achieving this capability requires a significant leap forward in our understanding and modeling of the additive manufacturing process. AM inherently involves a complex, coupled system of multi-physics phenomena (including thermal, mechanical, and chemical interactions) across multiple time and length scales. The final material structure and performance are influenced by a vast number of process parameters—laser power, scan speed, powder morphology, ambient conditions, and more—making predictive simulation a formidable but essential challenge.

This special issue is focused on recent advances in the development and application of cutting-edge computational methods for modeling additive manufacturing processes. Topics of interest include, but are not limited to, high-fidelity process simulations, multiscale and multiphysics modeling frameworks, machine learning-augmented simulation, uncertainty quantification, and efforts to integrate simulations with experimental validation. Contributions that advance the predictive capability and scalability of AM simulations, particularly those that can inform process optimization and material qualification, are especially encouraged.

Manuscript submission information:

Manuscript submission deadline: 30-Mar-2026

Contributions to this Special Issue will be by invitation only.

Keywords:

Level-Set, Phase-Field, Statistical Mechanics, Crystal Plasticity, Solidification

Special Issue on Honor of Peter Lax for his profound contributions to computational science
Día de Entrega: 2026-03-31

This special issue is organized to honor the profound contributions of Peter Lax, who passed away recently. His work has had a lasting impact on computational science, and we hope this Special Issue will serve as a meaningful tribute to his legacy.

Guest editors:

Prof. Frederic Gibou
University of California Santa Barbara, Santa Barbara, California, United States

Dr. Stanley J. Osher
University of California Los Angeles, Los Angeles, California, United States

Dr. Russ Caflisch
New York University Courant Institute of Mathematical Sciences, New York, United States of America

Dr. Chi-Wang Shu
Brown University, Providence, Rhode Island, United States

Manuscript submission information:

Manuscript submission open date: 1-Jul-2025
Manuscript submission deadline: 31-Mar-2026

Contributions to this Special Issue will be by invitation only.

Keywords:

numerical solution of PDEs, stability of algorithms, shock wave calculations

Special Issue on Advances in Computational Energy Science: Science of AI in Energy and Energy-Stable Computational Science
Día de Entrega: 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)