Data-Driven Modelling and Optimisation with Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 44
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the emerging frontiers of data-driven modelling and optimisation where classical assumptions no longer suffice—such as in high-dimensional, multi-agent, or physics-constrained systems. We aim to showcase work that leverages the underlying structure of complex systems—e.g., sparsity, symmetry, conservation laws—to design adaptive, interpretable, and scalable modelling and optimisation frameworks.
We invite contributions that go beyond black-box machine learning and instead integrate mathematical insight with data-driven innovation. Topics of interest include (but not limited to) the following:
- Physics-informed machine learning for inverse problems;
- Learning-to-optimise and theory of data-driven optimisers;
- Structure-aware algorithms for reinforcement learning and agent-based models;
- Theoretical foundations for deep learning and physics-informed learning on manifolds;
- Scalable optimisation in high dimensions;
- Interpretable and structure-aware AI models.
This Issue aims to engage applied mathematicians, machine learning theorists, and domain scientists working at the interface of modelling, data, and optimisation.
Dr. Junqi Tang
Guest Editor
Manuscript Submission Information
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Keywords
- optimisation
- machine learning
- learning-to-optimise
- data-driven modelling
- explainable AI
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