Dimensions overall score 8.0

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• VIRSO achieves mean relative L2 errors below 1% on nuclear thermal-hydraulic benchmarks across reconstruction ratios from 47:1 to 156:1.result 95%
• VIRSO reduces the energy-delay product (EDP) from approximately 206 J·ms for a graph operator baseline to 10.1 J·ms on an NVIDIA H200.result 95%
• VIRSO provides sub-10 W power consumption and sub-second latency when implemented on an NVIDIA Jetson Orin Nano.technical 95%
• VIRSO uses a variable-connectivity algorithm called Variable KNN (V-KNN) for mesh-informed graph construction.method 90%
• VIRSO outperforms other benchmark operators while using fewer parameters.result 85%
• VIRSO's performance could degrade in contexts with excessively irregular geometries or very sparse data beyond tested limits.limitation 90%
• VIRSO combines spectral and spatial graph operations to improve accuracy while maintaining low computational demand suitable for edge devices.method 80%
• VIRSO presents compute-aware operator learning as a new paradigm for real-time sensing in inaccessible and resource-constrained environments.technical 75%

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