29 citations · 40 across the 12 of their papers we have counts for
13 papers
Real-Time Monitoring of MHD Liquid Metal Flows with Shallow Recurrent Decoders
Claudio Scardino, Stefano Riva, Carolina Introini +4
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 phen…
Photonuclear Neutron Production in OpenMC: Verification Against MCNPX, FLUKA, and a First-Collision Analytical Solution
Lorenzo Loi, Andrea Missaglia, Carolina Introini +4
The modeling of photonuclear reactions is increasingly important for applications involving high-energy photon fields, including accelerator-driven neutron sources, radiation shiel…
Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications
Stefano Riva, Carolina Introini, J. Nathan Kutz +1
In reactor physics, neutronics and multi-physics phenomena can be modelled at different fidelity levels. High-fidelity models based on the Boltzmann transport equation, multi-group…
pyforce-1.0.0: Python Framework for data-driven model Order Reduction of multi-physiCs problEms
Stefano Riva, Yantao Luo, Carolina Introini +1
pyforce is a Python package implementing Data-Driven Reduced Order Modelling techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. The…
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…
Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors
M. Lo Verso, C. Introini, E. Cervi +3
Magnetohydrodynamic (MHD) phenomena play a pivotal role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten…
Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics
M. Lo Verso, C. Introini, E. Cervi +3
Magnetohydrodynamic (MHD) effects play a key role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten salts…
Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Stefano Riva, Carolina Introini, Josè Nathan Kutz +1
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 learnin…
A Comparison of Parametric Dynamic Mode Decomposition Algorithms for Thermal-Hydraulics Applications
Stefano Riva, Andrea Missaglia, Carolina Introini +2
In recent years, algorithms aiming at learning models from available data have become quite popular due to two factors: 1) the significant developments in Artificial Intelligence t…
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
Stefano Riva, Andrea Missaglia, Carolina Introini +2
The Shallow Recurrent Decoder networks are a novel paradigm recently introduced for state estimation, combining sparse observations with high-dimensional model data. This architect…
Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
Stefano Riva, Carolina Introini, J. Nathan Kutz +1
The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent p…
Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors
Stefano Riva, Carolina Introini, Antonio Cammi +1
Reliable, real-time state estimation in nuclear reactors is of critical importance for monitoring, control and safety. It further empowers the development of digital twins that are…
Multi-Physics Model Bias Correction with Data-Driven Reduced Order Modelling Techniques: Application to Nuclear Case Studies
Stefano Riva, Carolina Introini, Antonio Cammi
Nowadays, interest in combining mathematical knowledge about phenomena and data from the physical system is growing. Past research was devoted to developing so-called high-fidelity…