Deep Learning for Digital Twin Models

Digital twin models are recently used in variety of research areas where they help to reduce operation costs and prevent possible malfunctions of real world devices.

Deep learning models are constantly growing in new aspects, both theoretical and practical, important for various applications in modern technology and industry. One of the recent trends is development is digital twin operation which combine robustness of machine learning techniques with complex real world phenomena. The main goal of this special session is to promote and advance research activities related to all facets of digital twin development by the use of deep learning.

Session organizers welcome high-quality original submissions related to digital twin concepts in combination with deep learning, reinforcement learning, autonomous learning, transfer learning or in hybrid constructions with other Artificial Intelligence solutions of neural networks, fuzzy and rule based systems, in both theoretical and practical aspects, oriented on future generation computing.

Scope

Topics include but are not limited to the following:

• Theoretical aspects of digital twin models and simulation environments.
• Evolutionary based acceleration of deep learning, reinforcement learning, transfer learning, neuro-fuzzy models and other ideas for optimized digital twin.
• Parallel deep learning algorithms for future generation digital twin computing.
• Elimination of noise, bias control, overlapping data clusters processing, statistical models and other data analytics for digital twin optimization.
• Applications of digital twin in expert systems, engineering problems, data mining and optimization for industry, finance, transport, logistics, economy, manufacturing, security, IoT, VR, robotics, healthcare, science, and other domains, etc.

Author submission information

During submission of your paper follow author instruction on Submission | IEEE WCCI 2024 website. Please specify in the system that your paper is intended for Special Session on Deep Learning for Digital Twin Models.

30.06 – 05.07 2024, Yokohama, Japan

Marcin Woźniak

Full Prof., D.Sc., Ph.D., Eng. is with the Faculty of Applied Mathematics, Silesian University of Technology, Poland.

Marcin Woźniak received the M.Sc. degree in applied mathematics, the Ph.D. degree in computational intelligence, the D.Sc. degree in computational intelligence and Full Professor honours from the President of Poland. M. Wozniak is currently a Full Professor with the Faculty of Applied Mathematics, Silesian University of Technology.

He is a Scientific Supervisor in editions of "The Diamond Grant" and "The Best of the Best" programs for highly talented students from the Polish Ministry of Science and Higher Education. He participated in various scientific projects (as Lead Investigator, Scientific Investigator, Manager, Participant and Advisor) at Polish, Italian and Lithuanian universities and projects with applied results at IT industry both funded from the National Centre for Research and Development and abroad. He was a Visiting Researcher with universities in Italy, Sweden, and Germany.

He has authored/coauthored over 200 research papers in international conferences and journals. His current research interests include neural networks and fuzzy logic control systems with their applications together with various aspects of applied computational intelligence accelerated by evolutionary computation and federated learning models.

In 2017 he was awarded by the Polish Ministry of Science and Higher Education with a scholarship for an outstanding young scientist and in 2021 he received award from the Polish Ministry of Science and Higher Education for research achievements. From 2020 to 2023, each year Prof. M. Woźniak was presented among "TOP 2% Scientists in the World" by Stanford University for his career achievements.

Prof. Woźniak was the Editorial Board member or an Editor for Sensors, Machine Learning with Applications, Pattern Analysis and Applications, IEEE ACCESS, Measurement, Sustainable Energy Technologies and Assessments, Frontiers in Human Neuroscience, PeerJ CS, International Journal of Distributed Sensor Networks, Computational Intelligence and Neuroscience, Journal of Universal Computer Science, etc., and a Session Chair at various international conferences and symposiums, including IEEE Symposium Series on Computational Intelligence, IEEE Congress on Evolutionary Computation, etc.

For more information please visit: https://orcid.org/0000-0002-9073-5347

Jian Wang

Senior Member IEEE, is currently a Professor and servers as the Head of the Cross-Media Big Data Joint Laboratory with the College of Science, China University of Petroleum (East China), Qingdao, China. He received his Ph.D. degree in Computational Mathematics from Dalian University of Technology, China.

His research interests include computational intelligence, machine learning, pattern recognition, deep learning, differential programming, clustering, fuzzy systems, evolutionary computation. He was awarded several grants from the National Science Foundation of China, National Key Research and Development Program of China, Natural Science Foundation of Shandong Province, Fundamental Research Funds for the Central Universities. Prof. Wang serves as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, Information Sciences, International Journal of Machine Learning and Cybernetics, and Journal of Applied Computer Science Methods. He also serves on the Editorial Board for the Neural Computing & Applications and Complex & Intelligent Systems. In addition, He has served as the General Chair, the Program Chair, and the Co-Program Chair of several conferences such as the International Symposium on New Trends in Computational Intelligence, IEEE Symposium Series on Computational Intelligence and International Symposium on Neural Networks.

Jacek Mańdziuk

Ph.D., D.Sc., is Full Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology (Warsaw / Poland), Head of Division of Artificial Intelligence and Computational Methods and Head of Doctoral Programme in Computer Science.

He is the author of 3 books and 150+ research papers, was General Co-Chair of the 2021 Congress on Evolutionary Computation (Krakow, Poland) and organizer and Chair of the annual IEEE SSCI Symposium on Computational Intelligence for Human-like Intelligence 2013-2021. He serves/served as Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS and the IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, and is founding chair of the IEEE ETTC Task Force on Towards Human-like Intelligence. His research interests include application of CI and AI methods to games, dynamic and bi-level optimization problems, human-machine co-learning and cooperation in problem solving. He is also interested in development of general-purpose human-like learning and problem-solving methods which involve intuition, creativity and multitasking.

For more information please visit http://www.mini.pw.edu.pl/~mandziuk

Neal N. Xiong

He is current a Professor, Computer Science Program Chair, at Department of Computer, Mathematical and Physical Sciences, Sul Ross State University, Alpine, TX 79830, USA. He received his both PhD degrees in Wuhan University (2007, about sensor system engineering), and Japan Advanced Institute of Science and Technology (2008, about dependable communication networks), respectively. Before he attended Sul Ross State University, he worked in Georgia State University, Northeastern State University, and Colorado Technical University (full professor about 5 years) about 15 years. His research interests include Cloud Computing, Security and Dependability, Parallel and Distributed Computing, Networks, and Optimization Theory.

Dr. Xiong is the Chair of “Trusted Cloud Computing” Task Force, IEEE Computational Intelligence Society (CIS), HYPERLINK "http://www.cs.gsu.edu/~cscnxx/index-TF.html" http://www.cs.gsu.edu/ ~cscnxx/ index-TF.html, and the Industry System Applications Technical Committee, HYPERLINK "http://ieee-cis.org/technical/isatc/" http://ieee-cis.org/technical/isatc/; He is a Senior member of IEEE Computer Society from 2012,

For more information please visit https://dblp.uni-trier.de/pid/x/NaixueXiong.html