Advances in Dynamical Systems: Modeling Time-Evolving Processes Under Uncertainty
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".
Deadline for manuscript submissions: 25 May 2025 | Viewed by 120
Special Issue Editor
Interests: mathematical modelling; dynamic Bayesian networks; modelling and simulation; computational mathematics; nonlinear dynamics; dynamical system
Special Issue Information
Dear Colleagues,
This Special Issue focuses on enhancing the modelling of evolving complex systems, particularly emphasising reasoning under uncertainty. This powerful approach combines dynamical systems with probabilistic reasoning. For instance, the Dynamical Bayesian Network (DBN) extends traditional dynamic systems by explicitly representing and managing the uncertainty inherent in many real-world processes.
Dynamical systems describe how a system's state changes over time, typically governed by differential equations or discrete-time models. However, many systems face noise, incomplete information, and uncertainty, necessitating probabilistic reasoning. DBNs address this by integrating dynamical systems with Bayesian inference, where the system's evolution is modelled probabilistically. In this framework, each state depends on its previous state, incorporating the Markov property. This allows for more robust modelling of time-evolving processes, even when the system’s future is uncertain or based on incomplete data.
This issue aims to advance the field's capacity to model real-world systems characterised by temporal evolution and uncertainty by bridging dynamical systems with Bayesian reasoning.
Dr. Claudio Arancibia-Ibarra
Guest Editor
Manuscript Submission Information
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Keywords
- applied mathematics
- dynamical systems
- population dynamics
- pattern formation
- reaction-diffusion equations
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