Development and Research
The ATML Visualization team is actively involved in the development of innovative methods that bridge the gap between simulation codes and visualization systems. By enabling seamless coupling, these methods enhance the efficiency and interactivity of data analysis workflows, particularly in high-performance computing (HPC) environments. Additionally, we are exploring the use of synthetic (artificially generated) images as input training data for machine learning workflows. This approach aims to improve the accuracy and robustness of machine learning models in scenarios where real-world data is scarce or incomplete.
Contributions to Funded Projects
Our expertise in visualization and interactive HPC has been instrumental in several funded research projects, spanning a wide range of scientific domains.
Ongoing Projects
Completed Projects
- Morphological and Functional precision Diagnostics of the Nose (Rhinodiagnost)
- Energy oriented Center of Excellence (EoCoE 2)
- Center of Excellence in Combustion (CoEC):