Manuscript Topics

The study of dynamical systems has experienced a substantial rise in recent years, driven by the need to model, explain, and forecast the evolution of complex phenomena across a wide range of scientific and engineering disciplines. This growing interest has led to an increase in research across diverse application fields, including the physical, biological, and social sciences, as well as agriculture, business, education, engineering, medicine, and public health. The relevance of such analyses lies in the need to accurately characterize the evolution of dynamic phenomena subject to both deterministic mechanisms and stochastic influences. In all cases, the central objective is to understand the principles governing their temporal behaviour, enabling reliable predictions of future dynamics.

This special issue aims to highlight advances in the stochastic modeling of dynamical systems from both theoretical and applied perspectives. Contributions that present novel stochastic formulations, methodological developments in inference and prediction, or rigorous analyses of stochastic dynamics, as well as applications to complex systems arising in physical, biological, social, and technological contexts are welcome.

Topics of interest include, but are not limited to:
• Modelling by stochastic processes
• Dynamic growth models
• Inference in stochastic processes
• Dynamic prediction
• Dynamical systems estimation
• Functional data models
• Computational methods for dynamical models
• Applications in survival theory, medicine, biosciences, risk theory, insurance, finance, engineering, and other application areas.

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