7 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
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…
Gappy local conformal auto-encoders for heterogeneous data fusion: in praise of rigidity
Erez Peterfreund, Iryna Burak, Ofir Lindenbaum +4
Fusing measurements from multiple, heterogeneous, partial sources, observing a common object or process, poses challenges due to the increasing availability of numbers and types of…
Sampling weights of deep neural networks
Erik Lien Bolager, Iryna Burak, Chinmay Datar +2
We introduce a probability distribution, combined with an efficient sampling algorithm, for weights and biases of fully-connected neural networks. In a supervised learning context,…