Omar Álvarez, a researcher at the Centre for Research in Information and Communication Technologies (CITIC) of the Universidade da Coruña, has been awarded the PhD Award at the 9th European-African Conference on Wind Engineering (EACWE 2025), recently held in Trondheim, Norway.

The award was granted for his presentation titled “Extreme compression of URANS flow data,” a work that forms part of his research line focused on advanced data compression and modeling techniques in wind engineering. His contribution has drawn significant attention from the scientific community due to its innovative approach and applicability in fluid dynamics simulations.

This presentation is also linked to the scientific article “Signed distance function–biased flow importance sampling for implicit neural compression of flow fields,” recently published in the journal Computer‐Aided Civil and Infrastructure Engineering, co-authored with Miguel Cid Montoya. This work strengthens Álvarez’s international profile in the fields of computational engineering and applied machine learning.

The article proposes an efficient solution to handle the massive volumes of data generated by advanced fluid dynamics simulations—especially relevant in fields such as civil, aerospace, and automotive engineering.

By applying a sampling method known as BiFIS (Signed Distance Function-biased Flow Importance Sampling)—a technique based on signed distance functions—the researchers achieved a drastic reduction in file size without compromising data accuracy. They used implicit neural representations, which allow the compression of flow data into smaller, more manageable image-like structures.

This approach enhances real-time storage and visualization of complex simulations and improves the performance of image-based predictive models.

The article is available here.