1 citations · 1 across the 1 of their papers we have counts for
2 papers
physics.comp-ph2022★ 1 cited
Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations
Benjamin Wu, Oliver Hennigh, Jan Kautz +2
Physics-informed neural networks allow models to be trained by physical laws described by general nonlinear partial differential equations. However, traditional architectures strug…
cs.LG2020
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Jiahao Su, Wonmin Byeon, Jean Kossaifi +3
Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation.However, existing methods s…