3 citations · 5 across the 3 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2025★ 1 cited
Rapid training of Hamiltonian graph networks using random features
Atamert Rahma, Chinmay Datar, Ana Cukarska +1
Learning dynamical systems that respect physical symmetries and constraints remains a fundamental challenge in data-driven modeling. Integrating physical laws with graph neural net…
cs.LG2024★ 1 cited
Koopman-informed recurrent neural networks
Erik Lien Bolager, Ana Čukarska, Iryna Burak +2
Recurrent neural networks are a successful neural architecture for many time-dependent problems, including time series analysis, forecasting, and modeling of dynamical systems. In…