Session

DescriptionScientific advancement increasingly relies on the ability to process, transfer, and analyze massive data streams in near real time across distributed and heterogeneous computing systems. Fields such as high energy physics, climate modeling, bioimaging, and materials science face growing demands from high-resolution imaging, sensor-rich experiments, and simulation-driven digital twins, all of which require workflows that are resilient, scalable, and low-latency. This session tackles three core themes. First, resilient data management which addresses reliable movement, cataloging, and access of ever-growing datasets. Second, near–real-time workflows which explore low-latency streaming, analysis, and decision-making, highlighting strategies for heterogeneous architectures. Third, AI-driven modeling and digital twins which enable predictive workflow optimization and co-design of next-generation infrastructures. By connecting domain-specific challenges with generalizable solutions, the session showcases how integrated, intelligent approaches empower scalable, fault-tolerant scientific workflows and foster interdisciplinary collaboration, advancing the future of data-intensive discovery.