OVERVIEW OF DESIGN CONSIDERATIONS FOR DATA-DRIVEN TIME STEPPING SCHEMES APPLIED TO NON-LINEAR MECHANICAL SYSTEMS

Tomas Slimak, Andreas Zwölfer, Bojidar Todorov, Daniel J. Rixen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Artificial neural networks (NNs) are a type of machine learning (ML) algorithm that mimics the functioning of the human brain to learn and generalize patterns from large amounts of data without the need for explicit knowledge of the system’s physics. Employing NNs to predict time responses in the field of mechanical system dynamics is still in its infancy. The aim of this contribution is to give an overview of design considerations for NN-based time stepping schemes for non-linear mechanical systems. To this end numerous design parameters and choices available when creating a NN are presented and their effects on the accuracy of predicting the dynamics of non-linear mechanical systems are discussed. The findings are presented with the support of three test cases: a double pendulum, a duffing oscillator, and a gyroscope. Factors such as initial conditions, external forcing as well as system parameters were varied to demonstrate the robustness of the proposed approaches. Furthermore, practical design considerations such as noise-sensitivity as well as the ability to extrapolate are examined. Ultimately, we are able to show that NNs are capable of functioning as a time-stepping schemes for non-linear mechanical system dynamics applications.

Original languageEnglish
Title of host publication19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC)
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791887387
DOIs
StatePublished - 2023
EventASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023 - Boston, United States
Duration: 20 Aug 202323 Aug 2023

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume10

Conference

ConferenceASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Country/TerritoryUnited States
CityBoston
Period20/08/2323/08/23

Keywords

  • Machine Learning
  • Multibody Dynamics
  • Neural Network
  • Nonlinear Dynamics
  • Physics Informed

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