This work proposes a non-intrusive MOR method using generative machine learning by means of denoising diffusion probabilistic models (DDPMs) for generating solutions of the dynamical systems under different instances of their parameters to improve quality and temporal coherence.
Michiel Nikken, Nicolò Botteghi, Federico Califano et al.· 0 citations
An invertible AE architecture, named inv-AE, is proposed that computationally improves upon the stagnation of the reconstruction error typical of traditional AE architectures and can be combined with popular autoencoder-based ROM approaches, e.g., DL-ROM, to improve their accuracy.
Nicolò Botteghi, S. Glas, Christoph Brune· Computer Methods in Applied...· 1 citation
Numerical results demonstrate that HypEMBER consistently improves training stability and sample efficiency, while achieving superior robustness to uncertainties affecting both the system dynamics and the available observations, in comparison with state-of-the-art RL methods.
CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert, is investigated through simulation.
Steven Visch, Nicolò Botteghi, Antonio Franchi et al.· 0 citations
A reinforcement learning approach using the TD3 algorithm to train autonomous agents to reach arbitrary targets within a parametric, chaotic double-gyre flow is presented and a trade-off in sensor utility is revealed: velocity-aware agents optimize energy efficiency, whereas vorticity sensors provide superior structura...
Andrea Braghin, Nicolò Botteghi, Matteo Tomasetto et al.· 0 citations
PEARL employs an actor-adjoint algorithm that leverages automatic differentiation to compute policy gradients over short horizons and adjoint-based sensitivities of future returns approximated via neural networks, significantly reducing the number of environment interactions, while mitigating long-term gradient instabi...
