Begell House - Journal of Machine Learning for Modeling and Computing

Inicio > Portal Digitalde Begell > Revistas > Journal of Machine Learning for Modeling and Computing

ISSN Imprimir: 2689-3967

ISSN En Línea: 2689-3975

Journal of Machine Learning for Modeling and Computing

Editor-in-Chief: Dongbin Xiu

About Acceder

PUBLISHING DURING
COVID-19

Journal of Machine Learning for Modeling and Computing

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.

Objetivos y Alcance

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.

Current Archive

Latest Issue

IMPROVING NEURAL NETWORK PREDICTIONS OF MATERIAL PROPERTIES WITH LIMITED DATA USING TRANSFER LEARNING Schuyler Krawczuk, Daniele Venturi DYNAMIC MODE DECOMPOSITION FOR CONSTRUCTION OF REDUCED-ORDER MODELS OF HYPERBOLIC PROBLEMS WITH SHOCKS Hannah Lu, Daniel M. Tartakovsky

Artículos más Descargados

LIMITATIONS OF PHYSICS INFORMED MACHINE LEARNING FOR NONLINEAR TWO-PHASE TRANSPORT IN POROUS MEDIA Olga Fuks, Hamdi A. Tchelepi TENSOR BASIS GAUSSIAN PROCESS MODELS OF HYPERELASTIC MATERIALS Ari L. Frankel, Reese E. Jones, Laura P. Swiler MACHINE LEARNING FOR TRAJECTORIES OF PARAMETRIC NONLINEAR DYNAMICAL SYSTEMS Roland Pulch, Maha Youssef LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY Xiaohan Fu, Lo-Bin Chang, Dongbin Xiu TRAINABILITY OF ReLU NETWORKS AND DATA-DEPENDENT INITIALIZATION Yeonjong Shin, George Em Karniadakis A SURVEY OF CONSTRAINED GAUSSIAN PROCESS REGRESSION: APPROACHES AND IMPLEMENTATION CHALLENGES Laura P. Swiler, Mamikon Gulian, Ari L. Frankel, Cosmin Safta, John D. Jakeman

Archive Articles

A SURVEY OF CONSTRAINED GAUSSIAN PROCESS REGRESSION: APPROACHES AND IMPLEMENTATION CHALLENGES Laura P. Swiler, Mamikon Gulian, Ari L. Frankel, Cosmin Safta, John D. Jakeman IMPROVING NEURAL NETWORK PREDICTIONS OF MATERIAL PROPERTIES WITH LIMITED DATA USING TRANSFER LEARNING Schuyler Krawczuk, Daniele Venturi Show all