CODEE Journal

•  
•  

The CODEE Journal is a peer-reviewed and open-access online journal that publishes original materials promoting the teaching and learning of ordinary differential equations. All published papers are freely and permanently available online. Publishing open access enhances the visibility and impact of your research by making it freely and widely accessible to scholars, institutions, and funding organizations from around the world.

CODEE is seeking submissions for a 2026 Special Issue with a theme on Data-driven Modeling with Differential Equations.

Dr. Panayotova and Dr. Savatorova are serving as Editors for this issue.

Objective of the Special Issue

In the age of data-driven decision making, differential equations remain a vital tool for modeling dynamic processes, especially when integrated with modern data science techniques. From parameter estimation and model fitting to forecasting and simulation, ordinary differential equations (ODEs) offer a rigorous framework for analyzing real-world data across many fields. Integrating real-world data into ODE coursework offers a powerful way to enhance student learning by making mathematical modeling more relevant, engaging, and applicable.

This special issue of CODEE Journal invites submissions that explore the intersection of differential equations and data analysis. We seek scholarly contributions focused on undergraduate education and research that demonstrate how ODEs can be used to extract insight from data, support predictive modeling, and inform analysis in areas such as epidemiology, ecology, climate science, economics, public health, engineering, and beyond. Engaging students in modeling and data analysis using differential equations helps bridge theoretical mathematics with real-world challenges and builds essential skills for today’s data-rich world.

Topics of interest include (but are not limited to):

• Data-driven parameter estimation and model calibration
• Integration of ODEs with statistical, Bayesian, or machine learning methods
• Hybrid models combining first-principles ODEs with machine learning components
• Real-world applications informed by empirical data
• Pharmacokinetics, tumor growth, or glucose-insulin regulation using clinical data
• Dynamical systems models in environmental science, epidemiology, engineering (e.g., control systems, chemical processes), and neuroscience
• Using differential equations as interpretable alternatives to black-box time-series models
• Innovative teaching techniques and strategies for integrating ODEs and data science in undergraduate education

Educators, researchers, and students are all invited to contribute to this issue.

The submission deadline is 1 April 2026. You may send your manuscript now or up until the deadline, as papers will be published on an ongoing basis following peer review. All manuscripts should be submitted here. We also highly recommend authors to send a short abstract and tentative title in advance before sending the full manuscript to:

All submitted papers will be reviewed on a double-blind review basis. Submitted manuscripts should not be under consideration for publication elsewhere. Please send your manuscript as a Latex file. CODEE Journal LaTeX style files and sample documents for the CODEE Journal are located here.

Inquiries should be sent electronically to Dr. Panayotova, .

Important Dates:
Final date for submission	1 April 2026
Final date for the review results returned to authors	1 June 2026
Final date for revised manuscript submission	1 August 2026
Target date for online publication not later than	1 October 2026