Ermakov--Based embeddings as a parameter-free layer for physics-informed neural networks
Evolution equations with time-dependent coefficients force a neural solver to spend capacity on a scale that the equation already determines. We show that a single classical object, the Ermakov-Pinney scale function $\rho$, can be written into the input layer of a physics-informed neural network as a parameter-free emb...
R. Bernal-Jaquez, Roberto Romero Huerta, A. Schaum
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