Manuscript Topics

The growing complexity of multi-scale, nonlinear, and data rich systems in science and engineering poses significant challenges for modeling, simulation, and prediction. At the same time, advances in computational resources, sensing technologies, and data availability have created unprecedented opportunities to integrate physics based models with data driven methodologies. Model reduction, data assimilation, and machine learning have emerged as key tools for building efficient, reliable, and interpretable models of complex systems.

This special issue aims to highlight recent theoretical, methodological, and computational advances at the intersection of reduced order modeling (ROM), data assimilation (DA), and scientific machine learning (SciML). We seek contributions that develop novel frameworks, rigorous analysis, and practical algorithms for high-dimensional, multi-scale, and nonlinear systems, as well as applications in engineering, physical sciences, environmental and earth sciences, and beyond.

We particularly welcome synergistic approaches that combine physics based modeling, statistical inference, and machine learning to improve computational efficiency, predictive accuracy, robustness, and uncertainty quantification.

Topics of interest include, but are not limited to:

1. Model reduction techniques for complex dynamical systems  
2. Projection based and data driven reduced models (e.g., POD, DMD, operator inference)  
3. Data assimilation methods (e.g., Kalman filtering, ensemble methods, variational approaches)  
4. Scientific machine learning and physics informed learning methods  
5. Hybrid physics–data models and operator learning  
6. Inverse problems and parameter estimation in complex systems  
7. Multi-scale and multi-fidelity modeling  
8. Real time prediction, control, and digital twins  
9. Optimization and control of reduced and data enhanced systems

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Please submit your manuscript to online submission system
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