IMS2026 Technical Program

Recent advances in artificial intelligence (AI) and machine learning (ML) are transforming the way wireless components and complex electromagnetic (EM) systems are conceived, designed, and deployed. This session explores how ML-enabled optimization techniques are redefining applied electromagnetics, spanning the full pipeline from computational electromagnetics (CEM), uncertainty quantification (UQ), and antenna design to impactful applications such as magnetic resonance imaging (MRI), orthopaedic diagnostics, and remote sensing of snow and environmental parameters. By embedding AI and ML into EM modeling and optimization workflows, engineers can accelerate design cycles, navigate high-dimensional design spaces, and achieve performance levels that are difficult to reach with conventional approaches.
Beyond algorithms, the session emphasizes the critical role of data in driving the quality, robustness, and trustworthiness of AI-based solutions. High-fidelity simulation data, measurement-driven datasets, and hybrid physics-informed approaches are discussed as essential enablers for reliable learning and generalization. Attention is also given to the challenge of bridging ambition and deployment—moving AI-enhanced EM techniques from proof-of-concept demonstrations to deployable, validated systems operating under real-world constraints.