COSMIC-AI
Chaotic System Modeling with Intelligence & Computation

Harnessing AI to Decode Chaos, Powering the Future of Intelligent Systems
Project Name: Discovery of Chaotic Systems with Artificial Intelligence: Development, Analysis and Practical Applications of New Models
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​Abstract: Modeling of real-world systems plays an important role in many different fields. However, real-world systems are difficult to analyze because they are complex, dynamic, and uncertain. Since chaotic systems are generally defined as complex, dynamic and uncertain systems, chaos theory is widely used in many disciplines to model real-world systems. For this reason, the discovery of new chaotic systems; It has always been a hot research topic for science and engineering fields. Because it provides benefits such as a better understanding of the natural world, better solutions to engineering problems and the development of basic sciences. However, discovering a new chaotic system and analyzing the chaotic nature of existing systems in detail is a challenging process. The project proposal aims to use artificial intelligence techniques to address this difficult process. In this context, the main research question is "Can a chaotic system generator be designed based on artificial intelligence techniques?" has been. The aim of the project proposal is to develop an artificial intelligence model that can produce new chaotic systems (by learning) from existing chaotic systems. 

News

  • COST Action CA20110 exRNA-PATH RNA communication across kingdoms: new mechanisms and strategies in pathogen control, Hamburg Meeting – ExRNA research and innovation to improve human and plant health Hamburg, Germany, 11th-13th June2025
  • COST Action CA21169 DYNALIFE, General Conference: Theoretical Biology Meets Big Data: Dynamical Systems, Machine Learning, and Applications in Life Sciences, 3-5 June 2025, İstanbul
    • Action Web Page (link)
    • BioKey: DNA-Parametrized Multi-Chaotic System Encryption for Enhanced Security (pdf)
  • SCI Paper: A novel post-processing approach for improving minimum nonlinearity in S-boxes: A case study based on 2D hyper chaotic Schaffer transformation map (link)
  • SCI Paper: A Novel Chaotic System Generator Architecture Based on Optimization Algorithm (link)
  • COST Action CA21160 Non-globular proteins in the era of Machine Learning (ML4NGP), 3rd MEETING ON MACHINE LEARNING AND NON-GLOBULAR PROTEINS, 20 – 23 May 2025, Vilnius, LITHUANIA
  • ​COST Action CA21169 DYNALIFE Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES
    • Action Web Page (link)
    • Limitations of True Randomness in Quantum Integrable Systems: A Theoretical Investigation for Cryptographic Applications (pdf)
  • SCI Paper: An efficient 2D hyper chaos and DNA encoding-based s-box generation method using chaotic evolutionary improvement algorithm for nonlinearity (link)
  • SCI Paper: A new chaotic system and its practical applications in substitution box and random number generator (link)