M:3L Lab
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M:3L research

Metamaterials and Wave Control

M:3L studies inverse design of elastic metamaterials, wave-cloaking structures, and programmable wave propagation.

Overview

The laboratory combines differentiable FEM, neural-field optimization, and generative design to create manufacturable microstructures for broadband Rayleigh-wave cloaking and chiral metamaterials.

M:3L also collaborates internationally on machine-learning methods for mechanical structure generation and elastic wave control, including work presented at METAMAT2026 in Ajaccio, Corsica, France.

Demonstrated M:3L research

  • An inverse-design program for elastic metamaterials and wave-cloaking structures using differentiable FEM, neural fields, and generative design.
  • Work with Imperial College London on machine-learning methods for mechanical structure generation and elastic wave control, presented at METAMAT2026.

Technical capabilities

  • Elastic metamaterial inverse design
  • Broadband Rayleigh-wave cloaking
  • Chiral metamaterial design
  • Mechanical structure generation
  • Programmable elastic wave propagation

Connection to scientific ML

M:3L connects metamaterial design to scientific machine learning by evaluating learned or generated structures with mechanics simulation and physical constraints. Differentiable FEM and numerical optimization provide a path from a target wave response back to material or geometric design variables.

Potential applications are distinguished from demonstrated work: current public M:3L evidence covers elastic metamaterials, Rayleigh-wave cloaking, chiral structures, mechanical-structure generation, and elastic wave control; it does not establish deployment in a commercial material system.

Research directions

  • Broadband Rayleigh-wave cloaking and surface-wave control
  • Chiral elastic metamaterials
  • Manufacturable microstructure generation
  • Transformation-elastodynamics-inspired design
  • FEM-based and ML-assisted inverse optimization

Potential applications

  • Wave attenuation and redirection
  • Vibration and surface-wave control
  • Mechanically programmable materials
  • Material and microstructure design
  • Scientific-computing testbeds for inverse design

These are relevant application domains, not claims that every application is deployed.

Connected research

  • Differentiable Finite Element Methods: M:3L uses differentiable finite element methods to connect mechanics simulation with gradient-based optimization and machine learning.
  • Physics-Driven Machine Learning: M:3L develops machine-learning systems constrained and guided by physical laws for computational mechanics and scientific computing.
  • Agentic AI: M:3L develops autonomous, tool-using single-agent and multi-agent AI systems for scientific, engineering, and enterprise workflows.

Related projects and news

M:3L Secures HTI AI Virtual Institute Grant for High-Performance GPU Computing

M:3L has received access to NVIDIA H100 GPUs through the AI Virtual Institute, a program of Armenia's Ministry of High-Tech Industry. This support expands our capacity to design elastic metamaterials and wave-cloaking structures. The research combines differentiable FEM, neural-field optimisation, and generative design to create manufacturable microstructures for broadband Rayleigh-wave cloaking and chiral metamaterials.

M:3L Lab at METAMAT2026 in Ajaccio, Corsica

Our team joined METAMAT2026 in Ajaccio, Corsica, France. David Aznaurov presented ongoing work with Imperial College London on machine-learning methods for mechanical structure generation and elastic wave control, alongside five days of exchange on acoustic, mechanical, and thermal metamaterials.

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