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This paper investigates how shared spatial computation in neural operators can reduce energy consumption for virtual sensing applications, particularly in nuclear energy systems, while maintaining prediction accuracy.
This paper proposes a Sparse-Activation-ReLU (SAR) layer for low-latency, energy-efficient virtual sensing, achieving significant improvements in latency-error-energy metrics and reducing errors through synthetic knowledge distillation.
This paper proposes a simulation-based methodology to generate augmented traffic datasets by replacing physical sensors with virtual ones, extending sensor coverage in urban networks while preserving traffic patterns.
SeT-Diff proposes the first foundation model for HPC telemetry, using diffusion conditioned on semantic sensor descriptions to enable zero-shot generalization across tasks like imputation, forecasting, and virtual sensing, achieving an MAE of 0.0470 on reconstruction.