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Efficient nonlinear manifold reduced order model
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i. e., the solution space has a small Kolmogorov n-width.
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
A speedup of up to 2. 6 for 1D Burgers' and a speedup of 11. 7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique.
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