Situ Visualization

In-situ visualization aims to improve the analysis of large-scale simulations and complex processes by integrating visualization directly within the computational environment. Current research focuses on enhancing this process through techniques like implicit neural representations for efficient data compression and management, leveraging machine learning models (e.g., graph neural networks) to predict critical regions requiring higher resolution visualization, and integrating advanced algorithms such as Dynamic Mode Decomposition for efficient data analysis. These advancements are significantly impacting fields like medical procedures, industrial assembly, and computational fluid dynamics by enabling more efficient data analysis, reducing storage needs, and improving the accuracy and timeliness of insights derived from simulations.