Abstract: We explore the concept of combining physical modeling of the piano with deep learning using methods from differentiable digital signal processing. The core of our proposed approach is a modal synthesis model for the piano string, which is combined with a linear filter to approximate the acoustic properties of a grand piano. In a preliminary experiment, we train a neural network to estimate an excitation signal for a string in an autoencoder setting and show that the system can match the spectral content of a given target note. Our differentiable piano model could be utilized in a multitude of music processing tasks, including sound matching, signal enhancement, or source separation.