13 citations · 33 across the 11 of their papers we have counts for
11 papers
Real-time optimal control with shallow recurrent decoder networks
Matteo Tomasetto, Francesco Braghin, J. Nathan Kutz +1
Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor cont…
Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems
Matteo Tomasetto, Nicolò Botteghi, Gabriele Bruni +1
Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems. However, RL algorithms are sample inefficient…
Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning
Andrea Maria Braghin, Nicolò Botteghi, Matteo Tomasetto +2
Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environme…
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
Stefano Riva, Carolina Introini, Antonio Cammi +13
The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these syst…
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov +11
Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardiz…
The Seismic Wavefield Common Task Framework
Alexey Yermakov, Yue Zhao, Marine Denolle +13
Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variabilit…
HypeMARL: Multi-Agent Reinforcement Learning For High-Dimensional, Parametric, and Distributed Systems
Nicolò Botteghi, Matteo Tomasetto, Urban Fasel +2
Deep reinforcement learning has recently emerged as a promising feedback control strategy for complex dynamical systems governed by partial differential equations (PDEs). When deal…
PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
David Ye, Jan Williams, Mars Gao +4
SHallow REcurrent Decoders (SHRED) provide a deep learning strategy for modeling high-dimensional dynamical systems and/or spatiotemporal data from dynamical system snapshot observ…
Reduced Order Modeling with Shallow Recurrent Decoder Networks
Matteo Tomasetto, Jan P. Williams, Francesco Braghin +2
Reduced Order Modeling is of paramount importance for efficiently inferring high-dimensional spatio-temporal fields in parametric contexts, enabling computationally tractable param…
Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
Matteo Tomasetto, Francesco Braghin, Andrea Manzoni
Continuous monitoring and real-time control of high-dimensional distributed systems are often crucial in applications to ensure a desired physical behavior, without degrading stabi…
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
Matteo Tomasetto, Andrea Manzoni, Francesco Braghin
Steering a system towards a desired target in a very short amount of time is challenging from a computational standpoint. Indeed, the intrinsically iterative nature of optimal cont…