Session

DescriptionAdvancement in computational power and high-speed networking is enabling a new model of scientific experiment, experiment-in-the-loop computing (EILC). In this model, simulation and/or learning modules are run as data is collected from observational and experimental sources. Presently, the amount and complexity of data generated by simulations and by observational and experimental sources, such as sensor networks and large-scale scientific facilities, continues to increase. Several research challenges exist, many of which are independent of the scientific application domain. New algorithms, including artificial intelligence and machine learning algorithms, to merge simulation ensembles and experimental data sets must be developed. Data transfer techniques and workflows must be constructed to control the ensembles and integrate simulated and observed data sets. The Workshop on Extreme-Scale Experiment-in-the-Loop Computing (XLOOP 2025) will be a unique opportunity to promote this interdisciplinary topic area. We invite papers, presentations, and participants from the physical and computer sciences.