A Taylor Based Sampling Scheme for Machine Learning in Computational Physics

Abstract

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.

Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019), Vancouver, Canada. arXiv admin note: substantial text overlap with arXiv:2101.07561