0626822954674a06ccd9c234e3f0d572-Supplemental-Conference.pdf

All neural networks used in this work are fully connected, feed-forward neural networks. First-order NODEs are used for single-cell data, while second NODEs are used for the synthetic example as well as the motion capture data. In the second-order NODEs, the initial velocities are predicted using a neural network with two hidden layers with 20 or 100 neurons depending on the dataset with ELU activation function. The main architecture to infer velocities (or accelerations) also contains two hidden layers of sizes 20 or 100 depending on the size of the input and ELU activation function. As an ODE solver, we use an explicit 5-th order Dormand-Prince solver commonly denoted by dopri5.