Manuscript Topics

The increasing complexity of real-world problems, along with ongoing improvements in computational resources and algorithmic methods, is motivating the development of synergistic approaches that integrate applied mathematics, statistics, and data science. Such cross-disciplinary methods have become essential for modeling, simulation, inference, and decision-making in modern scientific and engineering problems.

This special issue aims to advance methodological frameworks and enrich application spectra for reliable and efficient modeling, inference, analysis, and computation, based on mathematical and statistical principles as well as data-driven approaches. We particularly welcome synergistic methods integrating mathematical, statistical, and data-science approaches, as well as standalone methodological contributions that advance fundamental techniques in each field.

Topics of interest include, but are not limited to:  
1. Synergistic mathematical, statistical, and data-science methods and applications  
2. Numerical analysis and scientific computing  
3. Mathematical modeling and simulation of complex systems  
4. Statistical inference, uncertainty quantification, and stochastic modeling  
5. Scientific machine learning and hybrid model-data approaches  
6. Data-driven approaches, including applications in health and biomedical sciences  
7. Optimization, control, and decision-making  
8. Biostatistical methods and applications  
9. Inverse problems and reconstruction methods

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