TAE combines Tikhonov regularization with autoencoders and a data randomization strategy to learn forward and inverse surrogates from one sample, with linear error bounds and tests on heat inversion and Navier-Stokes reconstruction.
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
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TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems
TAE combines Tikhonov regularization with autoencoders and a data randomization strategy to learn forward and inverse surrogates from one sample, with linear error bounds and tests on heat inversion and Navier-Stokes reconstruction.