Mathematical and Statistical Methods for Prediction and Optimisation in Artificial Intelligence
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 6
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue aims to showcase recent developments in mathematical and statistical methods that support prediction and optimisation within artificial intelligence (AI). As AI continues to transform sectors such as healthcare, transport, energy, finance, and environmental science, there is a growing demand for robust theoretical foundations and efficient computational techniques.
We welcome original research and review articles that advance mathematical modelling, statistical inference, and optimisation techniques with relevance to AI. Contributions may focus on novel theoretical insights, methodological innovations, or practical applications where mathematical and statistical principles enhance the performance, interpretability, or trustworthiness of AI systems.
We particularly encourage submissions that explore the intersection of prediction and optimisation under uncertainty, contribute to decision-making frameworks, or demonstrate impact in real-world scenarios.
Topics of interest include but are not limited to:
- Mathematical modelling for AI
- Statistical learning and inference in AI applications
- Predictive analytics using Bayesian or frequentist methods
- Optimisation algorithms for machine learning and data science
- Uncertainty quantification and decision support
- Interpretable and transparent AI
- Hybrid methods combining mathematics, statistics, and data-driven approaches
- Applications in health, climate, urban systems, finance, manufacturing, transport, and more fields
Prof. Dr. Diwei Zhou
Guest Editor
Manuscript Submission Information
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Keywords
- predictive modelling
- machine learning
- Bayesian inference
- uncertainty quantification
- convex and non-convex optimisation
- graph attention networks
- graph neural networks
- intelligent logistics and applications
- intelligent manufacturing
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