Matteo Tomasetto

According to our database, Matteo Tomasetto authored at least 8 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models.

Stefano Riva, Carolina Introini, Antonio Cammi, Dean Price, Alexey Yermakov, Yue Zhao, Philippe Martin Wyder, Judah Goldfeder, Jan P. Williams, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz

CoRR, May, 2026

2025

The Seismic Wavefield Common Task Framework.

Alexey Yermakov, Yue Zhao, Marine Denolle, Yiyu Ni, Philippe Martin Wyder, Judah Goldfeder, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz

CoRR, December, 2025

HypeMARL: Multi-Agent Reinforcement Learning For High-Dimensional, Parametric, and Distributed Systems.

Nicolò Botteghi, Matteo Tomasetto, Urban Fasel, Francesco Braghin, Andrea Manzoni

CoRR, September, 2025

PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery.

David Ye, Jan P. Williams, Mars Liyao Gao, Stefano Riva, Matteo Tomasetto, David Zoro, J. Nathan Kutz

CoRR, July, 2025

Reduced Order Modeling with Shallow Recurrent Decoder Networks.

Matteo Tomasetto, Jan P. Williams, Francesco Braghin, Andrea Manzoni, J. Nathan Kutz

CoRR, February, 2025

Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms.

Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov, Yue Zhao, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models.

Matteo Tomasetto, Francesco Braghin, Andrea Manzoni

CoRR, 2024

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models.

Matteo Tomasetto, Andrea Manzoni, Francesco Braghin

CoRR, 2024