M:3L research
M:3L uses differentiable finite element methods to connect mechanics simulation with gradient-based optimization and machine learning.
Differentiable FEM is part of the laboratory's physics-driven machine-learning work. Automatic differentiation through a finite element simulation allows gradients from an engineering objective to propagate toward design variables or machine-learning parameters.
This creates simulation-to-optimization loops that retain governing equations, boundary conditions, constitutive models, and finite element structure rather than replacing mechanics with an unconstrained predictor.
M:3L applies this capability to elastic metamaterials, wave-cloaking structures, neural-field optimization, and generative design of manufacturable microstructures.
A differentiable simulation exposes the sensitivity of an objective to upstream parameters. In an engineering loop, an objective such as wave attenuation, stiffness, displacement, or another response can be differentiated through the numerical model so an optimizer can update geometry, material parameters, boundary conditions, or learned representations.
These are relevant application domains, not claims that every application is deployed.
David Aznaurov, Davit Piliposyan, Danila Rukhovich, Sébastien Guenneau
A neural-field and differentiable-FEM framework for designing broadband Rayleigh-wave carpet cloaks within realizable Cauchy materials and mapping the optimized fields to explicit manufacturable microstructures.
We have published a preprint on arXiv with Imperial College London collaborator Sébastien Guenneau. The paper presents a neural-field and differentiable-FEM framework for designing broadband Rayleigh-wave carpet cloaks using physically realizable Cauchy materials. It then maps the optimized fields to explicit manufacturable microstructures through conditional diffusion and inverse design. In finite element validation, the homogenized microstructure model recovers approximately 97% of the defect-free reference surface-displacement magnitude, while the fully resolved geometry recovers approximately 76%.
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.