Diamond open access · Peer-reviewed · Reproducible research
Methods that are computationally practical
and analytically understood.
Original research at the interface of mathematical optimization, numerical analysis and machine learning, with open code and data encouraged.
Current Issue
Vol. 1 No. 1 (2026)
- Diamond open accessNo fees for authors or readers
- Single-anonymisedPeer review by two reviewers
- About 2 weeksTo first editorial decision
- 3150-0331e-ISSN
From the current issue
Vol. 1 No. 1 (2026)
Published:
2026-06-30
Research Articles
About the Journal
The Journal of Computational Optimization and Scientific Machine Learning (JCoSML) is an international, peer-reviewed, diamond open-access journal publishing original research at the interface of mathematical optimization, numerical analysis, and machine learning. Its scope covers continuous, discrete, and stochastic optimization; scientific machine learning, including physics-informed neural networks, neural operators, and surrogate modelling; optimization and computational methods for large language models and foundation models; numerical methods for differential equations in which a learning component plays a substantive role; inverse problems and uncertainty quantification; and the theoretical analysis of learning algorithms.
The journal favours contributions that are both computationally practical and analytically grounded, and places particular weight on reproducibility: open code, open data, and honest reporting of negative results are actively encouraged. JCoSML is free to publish and free to read, and authors retain copyright under a Creative Commons Attribution-NonCommercial 4.0 International licence (CC BY-NC 4.0).
