Begell House - Journal of Machine Learning for Modeling and Computing

Journal of Machine Learning for Modeling and Computing

Editor-in-Chief: Dongbin Xiu

ISSN Print: 2689-3967

ISSN Online: 2689-3975

Aims and Scope

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.

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

Current Forthcoming Archive

Most Downloaded Articles

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 MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE Ari L. Frankel, Cosmin Safta, Coleman Alleman, Reese Jones DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS Hannah Lu, Dinara Ermakova, Haruko Murakami Wainwright, Liange Zheng, Daniel M. Tartakovsky LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY Xiaohan Fu, Lo-Bin Chang, Dongbin Xiu FULLY CONVOLUTIONAL SPATIO-TEMPORAL MODELS FOR REPRESENTATION LEARNING IN PLASMA SCIENCE Ge Dong, Kyle Gerard Felker, Alexey Svyatkovskiy, William Tang, Julian Kates-Harbeck

Forthcoming Articles

A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables Reese Jones, Ari Frankel, Kyle Johnson Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression Tong Ma, David Barajas-Solano, Renke Huang, Alexandre Tartakovsky Show all

Archive Articles

MULTISTEP AND CONTINUOUS PHYSICS-INFORMED NEURAL NETWORK METHODS FOR LEARNING GOVERNING EQUATIONS AND CONSTITUTIVE RELATIONS Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky A NOTE ON APPLICABILITY OF ARTIFICIAL INTELLIGENCE TO CONSTITUTIVE MODELING OF GEOMATERIALS Mehdi Pouragha, Mahdad Eghbalian, Richard Wan Show all