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ROSE: RADICAL Orchestrator for Surrogate Exploration
SessionAI4S: 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
DescriptionScientific computing increasingly uses surrogate models to accelerate high-fidelity simulations, enable real-time predictions, and explore large design spaces. Building surrogates at scale is challenging: simulations are costly, data generation must be managed, and surrogate learning involves large, heterogeneous, evolving workflows. In active learning, where models guide data acquisition, these challenges intensify due to tight coupling between simulation, inference, and training. We present ROSE (RADICAL Orchestrator for Surrogate Exploration), a flexible, portable, and scalable framework supporting the full surrogate modeling lifecycle in HPC environments. ROSE integrates active learning with scalable orchestration, managing asynchronous execution across diverse resources while minimizing user effort. It supports in-situ/ex-situ workflows, online/offline training, and adaptive sampling. Applied to three use cases—electrolyte structure extraction, neutron diffraction structure recovery, and colloid phase classification—ROSE sustains high throughput with low overhead on Polaris, Perlmutter, and Delta, achieving 4–8× end-to-end speedups, with asynchronous orchestration delivering 1.5–3× gains over synchronous baselines.
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