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International Conference on Simulation-Based Optimization and Computational Modeling - (ICSBOCM-27)

26th - 27th June 2027 , Copenhagen - Denmark

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Call For Papers

The (ICSBOCM-27) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Computational Science, Data Science are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Simulation-based optimization techniques in AI
  • Computational modeling for complex systems
  • Real-time simulations in decision making
  • Data-driven approaches to optimization problems
  • Machine learning for simulation analysis
  • Challenges in computational modeling research
  • AI applications in engineering simulations
  • Predictive modeling for system performance
  • Collaborative simulation frameworks
  • Ethics in simulation-based research
  • Future trends in computational modeling
  • Data visualization for simulation results
  • Simulation techniques for risk assessment
  • Machine learning for optimization in logistics
  • Interdisciplinary approaches to simulation research
  • Impact of AI on simulation methodologies
  • Simulation-based decision support systems
  • Applications of optimization in healthcare
  • Statistical methods in simulation analysis
  • AI-driven simulations for environmental studies

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.