118 citations · 361 across the 15 of their papers we have counts for
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physics.flu-dyn2024
Mean flow data assimilation using physics-constrained Graph Neural Networks
M. Quattromini, M. A. Bucci, S. Cherubini +1
Despite their widespread use, purely data-driven methods often suffer from overfitting, lack of physical consistency, and high data dependency, particularly when physical constrain…
cs.LG2024
Neural delay differential equations: learning non-Markovian closures for partially known dynamical systems
Thibault Monsel, Onofrio Semeraro, Lionel Mathelin +1
Recent advances in learning dynamical systems from data have shown significant promise. However, many existing methods assume access to the full state of the system -- an assumptio…