Inverse Problems can be found in different branches of science. Many of them are related to estimating parameters in statistical models; for instance, a common practice is to tune parameters in partial differential equations or improve forecasts in sequential and variational data assimilation methods, all via noisy observations. Regardless of the context, many issues are raised during the estimation: the impact of sampling noise during the analysis, the non-linear relationship between observations and model variables, the computational cost of running numerical models, and the estimation of prior errors, among others. Machine Learning has become a powerful tool for parameter and state estimation in numerical models. In this context, we can find a pool of models that provide accurate estimates when combined with other information sources, for instance, numerical models. Besides, these methods can be employed to abstract error statistics from data and build statistical models that mimic the actual behavior of natural phenomena. In general, Machine Learning methods can be exploited in any context wherein complex relationships in variables (parameters or states) arise from data.
Special Issue Website
http://www.ceser.in/ceserp/index.php/ijai/about/editorialPolicies#custom-1
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. Latex templates can be download from the IJAI journal Guidelines.
Submission Link
https://forms.gle/18yrHacRWtfcBGbCA
List of Topics
The main topics of interest of this special issue are (but are not restricted to):
- Data Assimilation
- Inverse Problems
- Uncertainty Quantification
- Data-Driven Models
Guest Editor
Elias D. Nino-Ruiz, Ph.D.
Associate Professor, Department Chair
Department of Computer Science
Universidad del Norte
Barranquilla, Colombia
Tel: (+57) 5 3509509 Ext. 3261
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Website: https://aml-cs.github.io/
ORCID: http://orcid.org/0000-0001-7784-8163
Twitter: https://twitter.com/elias_david_84
Publication
Selected papers will be published in Autumn (October).
Contact
All questions about submissions should be emailed to Prof. Elías D. Nino-Ruiz - enino@uninorte.edu.co
About the Journal - International Journal of Artificial Intelligence™
The main aim of the International Journal of Artificial Intelligence™ (ISSN 0974-0635) is to publish refereed, well-written original research articles, and studies that describe the latest research and developments in the area of Artificial Intelligence. This is a broad-based journal covering all branches of Artificial Intelligence and its application in the following topics: Technology & Computing; Fuzzy Logic; Neural Networks; Reasoning and Evolution; Automatic Control; Mechatronics; Robotics; Parallel Processing; Programming Languages; Software & Hardware Architectures; CAD Design & Testing; Web Intelligence Applications; Computer Vision and Speech Understanding; Multimedia & Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Computational Theories of Learning; Signal, Image & Speech Processing; Intelligent System Architectures; Knowledge Representation; Bioinformatics; Natural Language Processing; Mathematics & Physics. The International Journal of Artificial Intelligence (IJAI) is a peer-reviewed online journal and is published in Spring and Autumn i.e. two times in a year. The International Journal of Artificial Intelligence (ISSN 0974-0635) was reviewed, abstracted and indexed in the past by the INSPEC The IET, SCOPUS (Elsevier Bibliographic Databases), Zentralblatt MATH (io-port.net) of European Mathematical Society, Indian Science Abstracts, getCITED, SCImago Journal & Country Rank, Newjour, JournalSeek, Math-jobs.com’s Journal Index, Academic keys, Ulrich’s Periodicals Directory, IndexCopernicus, and International Statistical Institute (ISI, Netherlands)Journal Index. The IJAI is already in request process to get reviewed, abstracted and indexed by the Clarivate Analytics Web of Science (Also known as Thomson ISI Web of Knowledge SCI), Mathematical Reviews and MathSciNet of American Mathematical Society, and by other agencies. Journal website: http://www.ceser.in/ceserp/index.php/ijai/index