Journal of Machine Learning for Modeling and Computing

10/15/2026

Important Update for Researchers Using the Open Science Framework (OSF):

The Center for Open Science has announced that it will phase out OSF Projects workflows between November 2026 and February 2027.

If you currently use OSF to share datasets or manage research projects and collaborative work, here's what you need to know:

📅 November 16, 2026: Users will no longer be able to create new projects or project components.

📅 February 19, 2027: All existing projects will become read-only. Content will remain accessible, but no further additions or edits will be permitted.

What should researchers do?

Review your existing projects and determine whether you need to update, download, or migrate any active research materials before these deadlines.

Importantly, OSF Registrations, Preregistrations, and Preprints are unaffected.

For complete details and guidance, visit:

https://help.osf.io/article/727-osf-projects-transition

This Article Is Licensed Under CCO For Maximum Reuse. Overview OSF Project workflows are being phased out between November 2026 and February 2027. Read more abo

10/14/2026

Engineering is constantly evolving—and staying current matters.

Each time you accept a review for a Begell House engineering journal, you'll receive **one month of complimentary access** to the journal's complete content.

It's an opportunity to discover new research, emerging technologies, and innovative engineering solutions while supporting your professional community.

Interested in becoming a reviewer? Submit your request: https://forms.gle/erxbxF4VJzWDp9CP9

10/06/2026

Federated Learning on Stochastic Neural Networks

https://www.dl.begellhouse.com/journals/558048804a15188a,1c7e14936ceba523,7d79b6394969fcf3.html

10/05/2026

🔬 New from Thermopedia: Toward Scalable Urban Heat Island Prediction: A Feasibility Study Using CFD-Informed Machine Learning for BIPV Design examines how machine learning can make urban overheating assessment faster and more practical.

Authored by Svetlana A. Tkachenko (The University of New South Wales), Hamidreza Mohammadpour (UNSW Sydney), Christophe Menezo (Université de Lyon; University Savoie Mont-Blanc), Stephanie Giroux-Julien (Université de Lyon), and Victoria Timchenko (UNSW Sydney).

CFD can pinpoint heat hotspots that threaten public health, thermal comfort, and rooftop PV performance, but it is often too computationally expensive for routine use. This study trains a random forest model on 240 CFD simulations of a residential district, predicting local temperature swings of up to 20°C with R² above 0.96. The results point toward scalable, data-driven tools for urban planning and heat island mitigation.

Read the full article to explore the methodology and results:🔗 https://thermopedia.com/content/10491/

09/30/2026

Numerical Splitting Schemes as the Cornerstone for Mini-Batch Optimization: On the Importance of Well-Balanced Methods

https://www.dl.begellhouse.com/journals/558048804a15188a,1c7e14936ceba523,245ac36574eb3c82.html

09/16/2026

Every engineering breakthrough begins with rigorous peer review.

Whether it's improving heat exchanger performance, advancing computational modeling, developing sustainable energy systems, or designing next-generation materials, peer review helps ensure engineering research is accurate, reliable, and ready to make an impact.

At Begell House, we're proud to recognize the engineers and researchers who dedicate their time to strengthening our journals through thoughtful peer review.

If you have expertise in engineering or applied sciences, we invite you to join our global reviewer community.

Learn more about a reviewer benefits: https://www.begellhouse.com/authors/for_reviewers.html

07/29/2026

Conformal Approach to Gaussian Process Surrogate Evaluation with Marginal Coverage Guarantees

https://dl.begellhouse.com/journals/558048804a15188a,24181d765dd9f652,349feb500f046e7a.html

07/23/2026

Time-Series Forecasting and Refinement Within a Multimodal PDE Foundation Model

https://dl.begellhouse.com/journals/558048804a15188a,42b2a89d00d62375,739909b82353ed3a.html

07/20/2026

Assessing ChatGPT for Coding Finite Element Methods

https://dl.begellhouse.com/journals/558048804a15188a,498820861ef102d2,17d7ce7550fe2f08.html

07/20/2026

The Journal of Machine Learning for Modeling and Computing provides researchers with a rigorous, peer-reviewed forum dedicated exclusively to the study of machine learning methods in scientific modeling and computation. Indexed in major databases including Clarivate, Scopus, EBSCO, and Google Scholar, the Journal of Machine Learning for Modeling and Computing ensures broad discoverability for work spanning real-world system modeling, novel numerical strategies, and the mathematical foundations of machine learning. Authors benefit from open access publishing options that maximize readership and citation potential across both the machine learning and scientific computing communities.

Publish with us: www.begellhouse.com/JMLMC

Editor-in-Chief: Dongbin Xiu (The Ohio State University)