  Machine Learning (ML) is increasingly used to construct surrogate models for
physical simulations. We take advantage of the ability to generate data using
numerical simulations programs to train ML models better and achieve accuracy
gain with no performance cost. We elaborate a new data sampling scheme based on
Taylor approximation to reduce the error of a Deep Neural Network (DNN) when
learning the solution of an ordinary differential equations (ODE) system.
