Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications
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 diffusion, or computational fluid dynamics are computationally demanding, whereas simplified models, such as zero-dimensional lumped formulations, can be evaluated efficiently at the cost of neglecting spatial details. The computational intractability of detailed models translates into a scarcity of high-fidelity data and an abundance of low-fidelity data, motivating the development of multi-fidelity (MF) learning strategies able to map between the two. This work extends Shallow Recurrent Decoders (SHRED), a ma...
| Authors | Stefano Riva, Carolina Introini, J. Nathan Kutz et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 22 May 2026 |
| Last updated at source | 27 July 2026 |
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