Author

S. Riva

3 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Oct 2026

Dynamics of the Molten Salt Fast Reactor Through Parametric Dynamic Mode Decomposition

Data-driven reduced-order modelling techniques are gaining increasing popularity as valuable tools to efficiently simulate the dynamics of complex systems across different operating conditions. In particular, they can be applied to perform multi-query scenario analyses, including sensitivity studies, where multiple e...

Mohammad Alrwashdeh, C. Introini, Claudio Stecchetti et al. · The Arabian journal for scie... · 0 citations

#machine learning Open access Oct 2025

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor

Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including u...

S. Riva, C. Introini, J. Kutz et al. · Chemical Engineering and Sci... · 4 citations

#machine learning Preprint Aug 2026

Real-Time Monitoring of MHD Liquid Metal Flows with Shallow Recurrent Decoders

State estimation in magnetohydrodynamic flows is critical for real-time monitoring of liquid metal blankets in tokamak fusion reactors. Due to the multiphysics nature of these phenomena, high-fidelity simulations are computationally prohibitive for real-time applications. This work investigates a data- driven Reduced O...

Claudio Scardino, S. Riva, C. Introini et al. · 0 citations