AI RESEARCH
Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning
arXiv CS.LG
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ArXi:2603.09032v1 Announce Type: new Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict energy and reliability constraints make centralized SciML implementation impractical. Most SciML models assume raw data aggregation at a central node, incurring prohibitively high communication latency and energy costs; yet, distributing models developed for general-purpose ML often breaks essential physical principles, resulting in degraded performance. To address these challenges, we.