arXiv AI By Giovanni B. Esposito, Francesco Antici, Daniele Cesarini, Andrea Bartolini

SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series

Read the original on arXiv AI →

arXiv:2607. 22548v1 Announce Type: new Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jun 19

When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting

arXiv:2606. 19363v1 Announce Type: new Abstract: The deployment of Time-Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade-off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero-shot to specific scientific domains, and their computational cost prohibits deployment in edge-computing sensor networks.

By Rupasree Dey, Abdul Matin, Nathan Orwick, Yao Zhang, Shrideep Pallickara, Sangmi Lee Pallickara