Begell House - Journal of Machine Learning for Modeling and Computing

Journal of Machine Learning for Modeling and Computing

Editor-in-Chief: Dongbin Xiu

ISSN Druckformat: 2689-3967

ISSN Online: 2689-3975

Ziele und Zweck

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.

Now Seeking Submission for Issue 4

Submission deadline: March 30, 2021
Decision expected by: May 31, 2021
Publication date: June 2021

Current Issues

Issue 3: Closed for submissions
Issue 2.

Issue 1.

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.

Enquiries can be directed to Editor-in-Chief Dongbin Xiu at xiu.16@osu.edu.

Current Forthcoming Archive

Am häufigste heruntergeladene Artikel

LIMITATIONS OF PHYSICS INFORMED MACHINE LEARNING FOR NONLINEAR TWO-PHASE TRANSPORT IN POROUS MEDIA Olga Fuks, Hamdi A. Tchelepi A SURVEY OF CONSTRAINED GAUSSIAN PROCESS REGRESSION: APPROACHES AND IMPLEMENTATION CHALLENGES Laura P. Swiler, Mamikon Gulian, Ari L. Frankel, Cosmin Safta, John D. Jakeman TENSOR BASIS GAUSSIAN PROCESS MODELS OF HYPERELASTIC MATERIALS Ari L. Frankel, Reese E. Jones, Laura P. Swiler DYNAMIC MODE DECOMPOSITION FOR CONSTRUCTION OF REDUCED-ORDER MODELS OF HYPERBOLIC PROBLEMS WITH SHOCKS Hannah Lu, Daniel M. Tartakovsky LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY Xiaohan Fu, Lo-Bin Chang, Dongbin Xiu MACHINE LEARNING FOR TRAJECTORIES OF PARAMETRIC NONLINEAR DYNAMICAL SYSTEMS Roland Pulch, Maha Youssef

Forthcoming Articles

Imputation of Contiguous Gaps and Extremes of Subhourly Groundwater Time Series Using Random Forests Dipankar Dwivedi, Utkarsh Mital, Boris Faybishenko, Baptiste Dafflon, Charuleka Varadharajan, Deborah Agarwal, Kenneth Hurst Williams, Carl Steefel, Susan Hubbard Show all

Archive Articles

DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS Hannah Lu, Dinara Ermakova, Haruko Murakami Wainwright, Liange Zheng, Daniel M. Tartakovsky DYNAMIC MODE DECOMPOSITION FOR CONSTRUCTION OF REDUCED-ORDER MODELS OF HYPERBOLIC PROBLEMS WITH SHOCKS Hannah Lu, Daniel M. Tartakovsky Show all