A neural network framework combines a non-differentiable approximation function with a smooth fluid reconstruction module to accurately reconstruct compressible flow fields with shocks.
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
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Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields
A neural network framework combines a non-differentiable approximation function with a smooth fluid reconstruction module to accurately reconstruct compressible flow fields with shocks.