Finalidades e escopo

The Journal of Machine Learning for Modeling and Computing (JMLMC) focuses on the study of machine learning methods for modeling and scientific computing. The scope of the journal includes, but is not limited to, research of the following types: (1) the use of machine learning techniques to model real-world problems such as physical systems, social sciences, biology, etc.; (2) the development of novel numerical strategies, in conjunction of machine learning methods, to facilitate practical computation; and (3) the fundamental mathematical and numerical analysis for understanding machine learning methods.

Call for Papers

The Journal of Machine Learning for Modeling and Computing (JMLMC) is seeking submissions from leaders in the field. If you would like to contribute, please submit your articles in Begell House submission site at Begell House Submission System

Please feel free to contact Editor-in-Chief Dongbin Xiu at xiu.16@osu.edu if you have any questions or need any assistance. Begell House can also be contacted at journals@begellhouse.com.

Manuscript Preparation

Author instructions for the Journal of Machine Learning for Modeling and Computing can be found at: Instruction.pdf.

As part of the community reciprocation that furthers research in any field, authors who submit articles to JMLMC acknowledge that they may be asked to review other articles for the journal.

Call for Papers: Special Issue on Advancements and Applications of Multifidelity Machine Learning in Engineering

Guest editors:

Negin Alemazkoor
Assistant Professor of Engineering Systems and Environment, University of Virginia, Charlottesville, VA
Email: na7fp@virginia.edu.

Submission Instructions

Manuscripts (original research or comprehensive review) must be prepared according to the Instructions to Authors on the journal website and submitted via the Begell House journal submission portal