ANRF Sponsored One Week National Level Workshop Titled “AI-Driven Antenna Design and Optimization by Integrating Machine Learning, Deep Learning, and Electromagnetic Intelligence for Future Wireless Systems", KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

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TNEA:2764 TNEA Counselling Code: 2764

Title
ANRF Sponsored One Week National Level Workshop Titled “AI-Driven Antenna Design and Optimization by Integrating Machine Learning, Deep Learning, and Electromagnetic Intelligence for Future Wireless Systems"

Hybrid Event
ANRF Sponsored One Week National Level Workshop Titled “AI-Driven Antenna Design and Optimization by Integrating Machine Learning, Deep Learning, and Electromagnetic Intelligence for Future Wireless Systems"
Workshop Dept. Level
DATE
Oct 27, 2025 to Oct 31, 2025
TIME
09:00 AM to 04:20 PM
DEPARTMENT
EC
TOTAL PARTICIPATES
61
ANRF Sponsored One Week National Level Workshop Titled “AI-Driven Antenna Design and Optimization by Integrating Machine Learning, Deep Learning, and Electromagnetic Intelligence for Future Wireless Systems" ANRF Sponsored One Week National Level Workshop Titled “AI-Driven Antenna Design and Optimization by Integrating Machine Learning, Deep Learning, and Electromagnetic Intelligence for Future Wireless Systems"
Summary

  • Skill Enhancement: Participants gained practical knowledge of integrating AI algorithms with EM simulators for rapid antenna prototyping and optimization.

  • Research Collaboration: The event encouraged interdisciplinary collaboration between antenna researchers, data scientists, and system designers.

  • Knowledge Dissemination: A digital repository containing lecture notes, recorded sessions, and example codes was shared with all participants.

  • Publication & Proposal Development: Several participants initiated collaborative ideas for publishing AI-EM co-design research papers and preparing project proposals under ANRF and SERB.

  • Industry Readiness: The workshop bridged theoretical learning with industrial practices in AI-driven electromagnetic design workflows.


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