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Deep learning methods have been employed in gravitational-wave astronomy toaccelerate the construction of surrogate waveforms for the inspiral ofspin-aligned black hole binaries, among other applications. We face thechallenge of modeling the residual error of an artificial neural network thatmodels the coefficients of the surrogate waveform expansion (especially thoseof the phase of the waveform) which we demonstrate has sufficient structure tobe learnable by a second network. Adding this second network, we were able toreduce the maximum mismatch for waveforms in a validation set by 13.4 times. Wealso explored several other ideas for improving the accuracy of the surrogatemodel, such as the exploitation of similarities between waveforms, theaugmentation of the training set, the dissection of the input space, usingdedicated networks per output coefficient and output augmentation. In severalcases, small improvements can be observed, but the most significant improvementstill comes from the addition of a second network that models the residualerror. Since the residual error for more general surrogate waveform models(when e.g., eccentricity is included) may also have a specific structure, onecan expect our method to be applicable to cases where the gain in accuracycould lead to significant gains in computational time.

Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling