2 citations · 3 across the 2 of their papers we have counts for
2 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…
math.NA2026★ 2 cited
Fast training of accurate physics-informed neural networks without gradient descent
Chinmay Datar, Taniya Kapoor, Abhishek Chandra +6
Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a p…