Real-data-driven real-time reconfigurable microwave reflective surface

Area of Science:

• Electromagnetics
• Materials Science
• Artificial Intelligence

Background:

• Controlling electromagnetic (EM) scattering from surfaces dynamically for stealth and communication is challenging with conventional methods.
• Existing techniques struggle with arbitrary design goals and real-world complexities.

Purpose of the Study:

• To present a reconfigurable conformal metasurface prototype and a workflow for dynamic EM scattering control.
• To enable response to multiple design targets with low on-site computing power and time.

Main Methods:

• A reconfigurable conformal metasurface prototype was developed.
• A sequential tandem neural network, pre-trained with experimental data, drives the metasurface.
• A data-driven workflow was employed, minimizing prior knowledge and human effort.

Main Results:

• The metasurface demonstrated dynamic control over EM reflection patterns.
• The system operated accurately in complex environments with varying incident angles and frequencies.
• Low on-site computing power and time were required for operation.

Conclusions:

• The proposed platform offers maximized versatility in reflection control, moving towards intelligent tunable EM surfaces.
• The data-driven approach overcomes limitations of traditional simulation and optimization techniques.
• This technology has significant implications for EM stealth and communication applications.