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- Istituto di Fotonica e NanotecnologieIT287 papers
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- Istituto Nazionale di Fisica Nucleare, Sezione di MilanoIT232 papers
- Sapienza University of RomeIT155 papers
- Italian Institute of TechnologyIT125 papers
- University of Milano-BicoccaIT115 papers
- National Research CouncilLK110 papers
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91 papers · 1 filter
Stability Analysis of Grid-Following and Grid-Forming Converters Connected to Generators
Alessandra Casiraghi, Marzio Barresi, Samuele Grillo
This work presents an examination of the main interactions between grid-following (GFL) and grid-forming (GFM) voltage source converters (VSCs) and synchronous generators (SGs), ca…
Homotopy-Guided Potential Games for Congestion-Aware Navigation
Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Lasse Peters, Michael Khayyat +3
We address the multi-agent motion planning problem where interactions, collisions, and congestion co-exist. Conventional game-theoretic planners capture interactions among agents b…
Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control
Roya Khalili Amirabadi, Mohsen Jalaeian Farimani, Omid Solaymani Fard
This paper proposes a novel reinforcement learning framework, named Self-Organizing Dual-buffer Adaptive Clustering Experience Replay (SODACER), designed to achieve safe and scalab…
Topology optimization of nonlinear forced response curves via reduction on spectral submanifolds
Hongming Liang, Matteo Pozzi, Jacopo Marconi +2
Forced response curves (FRCs) of nonlinear systems can exhibit complex behaviors, including hardening/softening behavior and bifurcations. Although topology optimization holds grea…
Distributed Koopman Operator Learning from Sequential Observations
Ali Azarbahram, Shenyu Liu, Gian Paolo Incremona
This paper presents a distributed Koopman operator learning framework for modeling unknown nonlinear dynamics using sequential observations from multiple agents. Each agent estimat…
A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method
Matteo Baù, Luca Perbellini, Samuele Grillo
Several methods have been proposed in the literature to improve the quality of AC optimal power flow (AC-OPF) datasets used in machine learning (ML) models. Yet, scalability to lar…