Erfan Asgari, a PhD student in the Renewable Materials for Healthy Built Environments programme at the University of Primorska, conducting his research at InnoRenew CoE, UP IAM, recently publisheda new scientific article, “Prediction, analysis, and improvement of sound insulation performance of membrane-type acoustic metamaterials using machine learning and preliminary experiments”, together with an international team of researchers in the journal Applied Acoustics. The publication is part of his PhD research at InnoRenew CoE, UP IAM.

The article introduces a machine learning-based framework for the rapid prediction and optimization of the sound insulation performance of membrane-type acoustic metamaterials (MAMs). By reducing the reliance on time-consuming numerical simulations, the developed models provide an efficient approach for exploring design possibilities and identifying the most influential parameters affecting acoustic performance.

The approach combines machine learning predictions with numerical simulations and experimental measurements to validate the reliability of the models. The results contribute to the development of data-driven methods for designing advanced acoustic materials and support the development of sustainable solutions for low-frequency noise control in buildings, transportation and other applications requiring effective sound insulation.