The International Journal of Computational Mechanics and Physics is dedicated to publishing high-quality original research articles, review papers, and impactful technical notes in all areas of computational physics and mechanics. The journal aims to advance the state of the art in computational methods, numerical algorithms, and their application to a broad spectrum of physical and engineering problems.

We particularly welcome contributions that deepen our understanding of complex physical systems through innovative computational approaches—especially those offering insights beyond the reach of purely theoretical or experimental methods.

Topics of Interest

The journal encompasses a wide range of topics, including but not limited to:

1. Numerical Methods and Algorithms
   • Development of novel algorithms for complex physical systems.
   • Advances in finite element, finite difference, finite volume, and spectral methods.
   • High-performance computing (HPC), parallel computing, and GPU acceleration.
2. Computational Fluid Dynamics (CFD)
   • Modeling and simulation of fluid flows, turbulence, multiphase, and reactive flows.
   • CFD applications in aerospace, energy, and environmental systems.
3. Computational Electromagnetics
   • Wave propagation, scattering, antenna design, and metamaterials.
   • Simulation techniques in optics, plasmonics, and photonics.
4. Quantum Mechanics and Quantum Computing
   • Simulation of quantum systems, quantum field theory, and many-body problems.
   • Quantum algorithms and their applications in physical sciences.
5. Molecular Dynamics and Materials Science
   • Atomistic simulations using molecular dynamics and Monte Carlo methods.
   • Computational studies of material properties, nanostructures, and soft matter.
6. Plasma Physics
   • Modeling of magnetic and inertial confinement fusion plasmas.
   • Simulations of space, astrophysical, and laboratory plasmas.
7. Computational Chemistry and Biophysics
   • Simulation of chemical reactions, protein folding, and biomolecular dynamics.
   • Computational approaches to drug design and molecular engineering.
8. Statistical Mechanics and Thermodynamics
   • Computational studies of phase transitions, critical phenomena, and non-equilibrium systems.
   • Applications of Monte Carlo and molecular dynamics in statistical physics.
9. Inverse Problems and Optimization
   • Computational techniques for solving inverse problems in physics and engineering.
   • Optimization methods for model calibration, control, and design.
10. Multiphysics and Multiscale Modeling
    • Integrated models coupling multiple physical phenomena and scales.
    • Hierarchical and concurrent multiscale simulation frameworks.
11. Machine Learning and Data-Driven Approaches
    • Use of machine learning and AI for physical modeling and simulation.
    • Surrogate modeling, reduced-order models, and data assimilation.
12. Visualization and Data Analysis
    • Advanced visualization of high-dimensional and large-scale data.
    • Computational tools for data interpretation and uncertainty quantification.