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.

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.

Call for Papers: Computational Modeling and Machine Learning Applications to Biological, Bio-inspired, and Epidemiological Systems

Guest Editors:

Padmanabhan Seshaiyer
Mathematical Sciences Department
George Mason University
Fairfax, VA, USA

Ritambhara Singh
Computer Science Department
Brown University
Providence, RI, USA

This special issue is devoted to sharing the development and application of new computational tools for discovering novel biological phenomena, rules, and theories. Specifically, articles that employ machine learning to study biological, bio-inspired, and epidemiological systems to build informative and predictive models of the underlying processes are invited. We hope this issue will encourage researchers to collaborate in biological investigations using novel machine learning tools to guide the exploration and discovery of new rules, phenomena, and theories in living systems.

Applications of machine learning to the following topics are welcome (but not limited to):
1. Multiscale modeling and data integration for biological systems
2. Applications to regulatory, structural, and functional genomics
3. Modeling epidemiological data to improve our understanding of public health outcomes
4. Complex biological systems at the molecular and cellular scales
5. Modeling and control of multiphysics describing biological phenomena
6. Investigating complex patterns and processes in ecology and evolutionary biology
7. Advancing novel multidisciplinary educational curriculum, training, and collaborations

Submission Deadline: October 15, 2022

Detailed instructions for submission can be found at:

https://www.j-mlmc.com/forauthors/.

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 MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE Ari L. Frankel, Cosmin Safta, Coleman Alleman, Reese E. Jones LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY Xiaohan Fu, Lo-Bin Chang, Dongbin Xiu DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS Hannah Lu, Dinara Ermakova, Haruko Murakami Wainwright, Liange Zheng, Daniel M. Tartakovsky hp-VARIATIONAL PHYSICS-INFORMED NEURAL NETWORKS FOR NONLINEAR TWO-PHASE TRANSPORT IN POROUS MEDIA Mingyuan Yang, John T. Foster

Forthcoming Articles

Modeling Unknown Dynamical Systems with Hidden Parameters Xiaohan Fu, Weize Mao, Lo-Bin Chang, Dongbin Xiu Show all

Archive Articles

LEVERAGING LOCAL VARIATION IN DATA: SAMPLING AND WEIGHTING SCHEMES FOR SUPERVISED DEEP LEARNING Paul Novello, G. Poëtte, D. Lugato, P. M. Congedo ELECTRIC LOAD AND POWER FORECASTING USING ENSEMBLE GAUSSIAN PROCESS REGRESSION T. Ma, David A. Barajas-Solano, R. Huang, Alexandre M. Tartakovsky Show all