5 papers
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…
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…
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…
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…
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…