3 papers
cs.AI2026
SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series
Giovanni B. Esposito, Francesco Antici, Daniele Cesarini +1
Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metr…
eess.SY2026
Physics-Informed Neural Networks for Nonlinear Output Regulation
Sebastiano Mengozzi, Giovanni B. Esposito, Michelangelo Bin +3
This work addresses the full-information output regulation problem for nonlinear systems, assuming the states of both the plant and the exosystem are known. In this setting, perfec…
cs.AI2026
SweetSpot: An Analytical Model for Predicting Energy Efficiency of LLM Inference
Hiari Pizzini Cavagna, Andrea Proia, Giacomo Madella +5
Large Language Models (LLMs) inference is central to modern AI applications, dominating worldwide datacenter workloads, making it critical to predict its energy footprint. Existing…