@inproceedings{9b55365bf52c4abe8092c31671455bbe,
title = "Machine Learning to Approximate Solutions of Ordinary Differential Equations: Neural Networks vs. Linear Regressors",
abstract = "We discuss surrogate models based on machine learning as approximation to the solution of an ordinary differential equation. Neural networks and a multivariate linear regressor are assessed for this application. Both of them show a satisfactory performance for the considered case study of a damped perturbed harmonic oscillator. The interface of the surrogate model is designed to work similar to a solver of an ordinary differential equation, respectively a simulation unit. Computational demand and accuracy in terms of local and global error are discussed. Parameter studies are performed to discuss the sensitivity of the method and to tune the performance.",
keywords = "Machine learning, Multivariate linear regressor, Neural network, Ordinary differential equations, Surrogate model",
author = "Georg Engel",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-22747-0_13",
language = "English",
isbn = "9783030227463",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag Italia",
pages = "169--177",
editor = "Rodrigues, {Jo{\~a}o M.F.} and Cardoso, {Pedro J.S.} and J{\^a}nio Monteiro and Roberto Lam and Krzhizhanovskaya, {Valeria V.} and Lees, {Michael H.} and Sloot, {Peter M.A.} and Dongarra, {Jack J.}",
booktitle = "Computational Science – ICCS 2019 - 19th International Conference, Proceedings",
address = "Italy",
note = "19th International Conference on Computational Science, ICCS 2019 ; Conference date: 12-06-2019 Through 14-06-2019",
}