ChEESE Research Takes the Stage at the International Conference on Underwater Acoustics in Glasgow

The conference brought together experts from around the world to discuss the latest advances in underwater acoustics, ranging from environmental monitoring and marine noise to wave propagation modelling and high-performance computing applications.

The ChEESE contributions showcased how advanced numerical methods and HPC can help address complex challenges in underwater and seismo-acoustic wave propagation. Maxence Ferrari, together with Vadim Monteiller, Paul Cristini, and Nathalie Favretto-Cristini, presented the work “Optimizing 3D Seismo-Acoustic Wave Propagation Modeling for Higher Frequencies: HPC Efficiency and Memory Gains”, focusing on strategies to improve computational performance and reduce memory requirements for large-scale simulations.

A second presentation by Nathalie Favretto-Cristini, Vadim Monteiller, Maxence Ferrari, and Paul Cristini, titled “Modeling Underwater Noise Pollution and Seabed Vibrations Using a 3D Full-Wave Method and HPC”, explored how high-fidelity numerical modelling can contribute to a better understanding of anthropogenic underwater noise and its interaction with the marine environment. Both sessions were attended by more than 100 people.

Both contributions highlight the role of high-performance computing in advancing wave propagation simulations across disciplines, from seismology to underwater acoustics. By improving computational efficiency while maintaining physical accuracy, this research supports the broader ChEESE mission of developing cutting-edge simulation capabilities for Earth and environmental sciences.

Hosted by the University of Strathclyde in the centre of Glasgow, ICUA 2026 provided an international forum for researchers, engineers, and industry experts to exchange ideas and discuss emerging challenges in underwater acoustics. The event continued the momentum of previous editions, reflecting the rapid developments taking place in the field and the growing importance of HPC-enabled modelling approaches.