semantic-conditioning

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.