Deadline Date: 31 January 2026
Computational mathematics is evolving rapidly, driven by the growing demand for accurate simulations, intelligent modelling, and real-time data analysis in science, engineering, and technology. With the integration of artificial intelligence, data science, and network analysis, modern mathematical computation is expanding its capabilities to address high-dimensional, nonlinear, and large-scale problems. Numerical methods continue to form the backbone of this field, enabling simulations in areas where analytical solutions are impractical. At the same time, advances in data-driven modelling, image reconstruction, inverse problems, and topological analysis are unlocking new possibilities in physical sciences, biology, medicine, and communication systems.
This Special Issue aims to bring together innovative research that bridges mathematical theory and computational practice. We invite contributions that focus on the development and application of advanced numerical algorithms, machine learning-integrated models, graph-based systems, and real-time simulation techniques. Emphasis is placed on interdisciplinary approaches that leverage the strengths of computational mathematics to solve complex real-world problems. Original research articles, review papers, and methodological innovations that demonstrate the transformative power of mathematical computation in imaging, modelling, and network analysis are especially encouraged.
Suggested Topics of Interest include (but are not limited to):
- AI-assisted numerical methods for simulation and system modelling
- Data-driven mathematical modelling in physical and biological sciences
- Computational imaging and inverse problem-solving techniques
- Machine learning integration with numerical analysis and optimization
- Graph theory and network-based modelling of complex systems
- Variational and PDE-based approaches in image and data analysis
- High-dimensional differential equations and approximation methods
- Uncertainty quantification and sensitivity analysis in computational models
- Topological and geometric data analysis using computational methods
- Tensor methods and multilinear algebra in large-scale data analytics
- Simulation of dynamic systems using hybrid AI and mathematical models
- Real-time computing and parallel algorithms for large-scale modelling
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Computational mathematics is evolving rapidly, driven by the growing demand for accurate simulations, intelligent modelling, and real-time data analysis in science, engineering, and technology. With the integration of artificial intelligence, ... show more
Editorial board
