Argonne National Laboratory (ANL), United States of America
Advancement 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 to merge simulation ensembles and experimental data sets, including artificial intelligence and machine learning, 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 2020) will be a unique opportunity to promote this interdisciplinary topic area. We invite papers, presentations and participants from the physical and computer sciences.