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Recent research in deep learning has shown that neural networks can learndifferential equations governing dynamical systems. In this paper, we adaptthis concept to Virtual Analog (VA) modeling to learn the ordinary differentialequations (ODEs) governing the first-order and the second-order diode clipper.The proposed models achieve performance comparable to state-of-the-artrecurrent neural networks (RNNs) albeit using fewer parameters. We show thatthis approach does not require oversampling and allows to increase the samplingrate after the training has completed, which results in increased accuracy.Using a sophisticated numerical solver allows to increase the accuracy at thecost of slower processing. ODEs learned this way do not require closed formsbut are still physically interpretable.

Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations