In recent years, digital twins have emerged as an advanced data-driven technology for modeling and monitoring complex physical systems to inform actionable decision-making. One area where digital twins can be especially impactful is urgent computing applications, such as natural disasters and hazards, where timely and reliable decision support is needed. However, it is challenging to streamline dynamic data with modeling and simulation methods for urgent computing applications due to the use of heterogeneous multimodal data sources, Internet of Things (IoT) networks, and high-performance computing (HPC) systems to power edge-to-cloud capabilities. In this work, we describe a reference architecture that uses federation and dynamic composability to power digital twins for complex physical systems. We demonstrate the usefulness of our approach with the Firemap architecture on WIFIRE, an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction, and visualization of wildfire behavior.

Distributed computing systems are essential for meeting the demands of modern HPC applications, yet transitioning from single-system to distributed environments presents significant challenges. Resource misallocation in shared systems can lead to resource contention, system instability, degraded performance, priority inversion, inefficient utilization, increased latency, and environmental impact. We present BanditWare, an online recommendation system that dynamically selects the most suitable hardware for applications using a contextual multi-armed bandit algorithm. We evaluated BanditWare on two workflow applications: BurnPro3D (a web-based platform for fire science), and a matrix multiplication application. Designed for seamless integration with the National Data Platform (NDP), BanditWare enables users of all experience levels to optimize resource allocation efficiently.