Articles, Resources and Links

Mathematical Modelling

Matti Heiliö et al., 2016

This book offers a comprehensive introduction to applying mathematics in real-world scenarios, highlighting the need for interdisciplinary input from fields like physics, computer science, and engineering. It combines classical modelling approaches with modern techniques such as soft computing, inverse problems, and model uncertainty, while exploring the interaction between models, data, and mathematical software.

Guide to Mathematical Modelling

Dilwyn Edwards, Mike Hamson, 1989

The authors convey their enthusiasm for the subject, engaging readers with practical modelling examples from everyday life, demonstrating the application of mathematical concepts to non-traditional scenarios. The book covers topics from modelling methodology and units of measurement to using data, random numbers, differential equations, and concludes with guidance on report writing and example models.

Modelling in Mathematical Programming

José Manuel García Sánchez, 2021

This book offers fundamental tools for mathematical programming, introducing a unique methodology for constructing integral models, ranging from simple to complex systems. It provides a structured approach for readers to build models by defining elements, variables, and constraints, and includes techniques for modelling optimisation objectives, helping to understand both new and existing models in the literature.

Mathematical Modelling by Help of Category Theory

Dmitrii Legatiuk, 2025

This monograph introduces a structural approach to engineering modelling using category theory, clarifying relationships between models and their complexities. It extends the theory to coupled models, incorporates engineering applications, and explores automatic model generation and error detection, offering valuable insights for researchers in applied mathematics and engineering.

Mathematical Modelling of Decision Problems

Nolberto Munier, 2021

This book serves as a guide to modelling complex problems in Multi Criteria Decision Making (MCDM), providing practical tips and examples for creating a decision matrix that reflects real-world scenarios. It features case studies solved using the SIMUS method, making it a valuable resource for practitioners, researchers, and students working with MCDM problems.

European Journal of Applied Mathematics

Since 2008, EJAM has expanded to include Applied and Industrial Mathematics, with a stronger emphasis on probabilistic applications. It publishes research inspired by real-world problems while fostering theoretical methods with broad applicability. Survey papers review emerging mathematical areas, while research papers focus on innovative mathematical ideas relevant to modelling, analysis, and modern science and technology.

Journal of the European Mathematical Society

JEMS, the official journal of the EMS, publishes high-quality research across pure and applied mathematics. Selected by a distinguished international editorial board, articles meet the highest academic standards. Occasionally, survey papers on significant topics are included. Established in 1999, it was published by Springer-Verlag until 2003 and has since been under the EMS Publishing House. Its editors-in-chief have included Jürgen Jost, Haïm Brezis, François Loeser, and Barbara Kaltenbacher.

Journal of Computational and Applied Mathematics

JCAM publishes original, high-value research in computational and applied mathematics, focusing on new computational techniques for scientific and engineering problems. Papers should analyse and prove computational efficiency, including stability and accuracy, and be supported by substantial numerical examples. Minor modifications of existing methods without significant advancements are not considered.

School on Mathematical Modelling, Numerical Analysis, and Scientific Computing

Organized under the auspices of the European Mathematical Society (EMS) and the Faculty of Mathematics and Physics at Charles University Prague, the event is organized by the Nečas Center for Mathematical Modelling.

Mathematics for Machine Learning Specialization

Learn the essential mathematics for machine learning, covering linear algebra, multivariate calculus, and dimensionality reduction to build a strong foundation for advanced studies.

Python for Data Science, AI & Development

Learn Python for data science and software development, covering programming fundamentals, key libraries like Pandas and NumPy, Jupyter Notebooks, and data retrieval through APIs and web scraping.

Practical Data Science with MATLAB Specialization

Learn MATLAB to analyse large datasets, clean and explore data using interactive tools, build and evaluate machine learning models, and create reports to share insights.