A loss decomposition that rewrites mean squared error into amplitude and phase terms is applied to a recurrent autoencoder for 1D linear advection, with claimed long-horizon accuracy gains shown on a single test case.
Combined space–time reduced-order model with three-dimensional deep convolution for extrapolating fluid dynamics
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Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks
A loss decomposition that rewrites mean squared error into amplitude and phase terms is applied to a recurrent autoencoder for 1D linear advection, with claimed long-horizon accuracy gains shown on a single test case.