1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2026★ 1 cited
Koopman-informed recurrent neural networks
Erik Lien Bolager, Ana Äukarska, 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…
cs.LG2026★ 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…
stat.CO2025
JaxSGMC: Modular stochastic gradient MCMC in JAX
Stephan Thaler, Paul Fuchs, Ana Cukarska +1
We present JaxSGMC, an application-agnostic library for stochastic gradient Markov chain Monte Carlo (SG-MCMC) in JAX. SG-MCMC schemes are uncertainty quantification (UQ) methods t…