ISSN 印刷: 2689-3967
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
Now Seeking Submissions for Issue 2
Submission deadline: June 30, 2020
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 firstname.lastname@example.org.
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.
LIMITATIONS OF PHYSICS INFORMED MACHINE LEARNING FOR NONLINEAR TWO-PHASE TRANSPORT IN POROUS MEDIA
MACHINE LEARNING FOR TRAJECTORIES OF PARAMETRIC NONLINEAR DYNAMICAL SYSTEMS
LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY
TENSOR BASIS GAUSSIAN PROCESS MODELS OF HYPERELASTIC MATERIALS
A SURVEY OF CONSTRAINED GAUSSIAN PROCESS REGRESSION: APPROACHES AND IMPLEMENTATION CHALLENGES
TRAINABILITY OF ReLU NETWORKS AND DATA-DEPENDENT INITIALIZATION