Insights & Publications
Technical perspectives on machine learning, electromagnetic simulation, and the future of high-frequency engineering.
Why AI Will Not Replace Electromagnetic Simulation
Artificial intelligence accelerates design exploration, but the complexities of Maxwell's equations and real-world physical constraints mean full-wave verification remains strictly necessary.
Surrogate Models for Faster RF Design-Space Exploration
How replacing computationally expensive full-wave simulations with trained surrogate models can dramatically reduce the time required for multi-dimensional RF optimization.
Understanding Multi-Objective Optimization in Antenna Design
Navigating the tradeoffs between bandwidth, gain, efficiency, and physical size using Pareto-front exploration.
From S11 to Radiation Efficiency: What an Antenna Optimizer Needs to Consider
Why optimizing purely for return loss (S11) can lead to highly matched but poorly radiating antennas.
Physics-Informed AI for RF and Electromagnetic Engineering
Integrating Maxwell's equations directly into machine learning models to ensure predictions obey the laws of physics.
Designing at mmWave Frequencies: Geometry Becomes Part of the Circuit
At millimeter-wave frequencies, parasitic coupling and manufacturing tolerances dominate performance, making parametric optimization essential.