Applied Physics at Scale.
Embedded Files
Intelligent RF & Metamaterial Systems
Intelligent RF & Metamaterial Systems
The 20th century transformed our understanding of physics. The 21st century became the era of digital services and artificial intelligence.
At Matterdyne, we are building for the next era — using AI to accelerate breakthroughs in applied physics, metamaterials, and secure RF and wireless systems
We develop tools for inverse design of metamaterials with customized electromagnetic properties, high-fidelity RF simulation, and secure wireless systems. Our work spans AI-driven metamaterial design, RF security tooling, and secure components for critical infrastructure.
Metamaterials
Metamaterials
Microscopic structures, such as repeating 3D-printed unit cells, can exhibit exotic physical properties that differ dramatically from their constituent materials.
Well-known examples include extreme stiffness at low density, negative refractive index, and auxetic behaviour (negative Poisson’s ratio).
At Matterdyne, we use AI-driven inverse design to engineer metamaterials with custom electromagnetic and thermal properties for next-generation RF systems and advanced engineering applications.
Metasurfaces
Metasurfaces
Engineered surfaces with sub-wavelength structures exhibit exotic electromagnetic properties not found in nature.
Applications include flat lenses, advanced beam steering, and ultra-thin radar-absorbing surfaces.
At Matterdyne, we develop AI-driven inverse design tools to engineer metasurfaces with custom electromagnetic responses, with particular focus on high-performance RF antennas and directional control for next-generation wireless systems.
GNSS Resilience with Directional Metasurfaces
Global Navigation Satellite Systems (GNSS) provide essential positioning, navigation, and timing for critical infrastructure, yet remain vulnerable to jamming and spoofing attacks.
We are developing directional metasurfaces as a physical-layer approach to improve GNSS resilience. By tailoring electromagnetic responses, we aim to enable adaptive beamforming and null steering. enhancing legitimate signals while suppressing interference from unwanted directions.
RF Device Spoofing Detection via AI-Driven Fingerprinting
Modern wireless systems face growing threats from RF device spoofing, where attackers impersonate legitimate hardware.
We are developing AI-driven spoof detection by learning unique physical-layer "fingerprints" from subtle transmitter hardware characteristics..
We aim to strengthen authentication at the signal level for critical infrastructure, communications, and secure IoT networks.
Page updated
Report abuse