Design of finite-size acoustic metamaterials with a combination of Machine Learning and Model Order Reduction
The objective of this research track is to optimize acoustic metamaterials of finite size with machine learning algorithms. While simple plate springs or C-shaped Helmholtz resonators are well-suited to study the basic principles, more complex unit cell realizations are required for a broadband, low-frequency attenuation. Although periodicity of the structure implies infinite structures, this is not the case for practical applications with the edges inducing a considerable effect. To effectively deal with the associated increased computational effort of modelling the cell multiple times, the DC will undertake MOR approaches in combination with machine learning strategies to result into performant numerical models that can be utilized in an optimization scheme. This research aims at incorporating MOR techniques into machine learning based optimization for a concurrent design of a host structure and a tailored metamaterial, aiming at automotive applications. An experimental validation (absorption coefficient in an impedance tube) of the designed structure (poroelastic core with the optimized inner resonator) will be done to validate the approach.
Research Topics
- Acoustic Metaporous Structures with Tunable Acoustic Properties (e.g., Bandgap, Sound Absorption, Wave Manipulation)
- Intelligent Optimization Algorithms for Inverse Problems
- Deep Learning-Based Approaches
- Normal Mode Decomposition-Based Inverse Identification in Multimodal Waveguides
- Model Order Reduction