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Physics-Informed Data-Driven Simulation of Constrained Multibody Systems

EasyChair Preprint no. 13605

2 pagesDate: June 9, 2024


We describe a framework that can integrate prior physical information, e.g., the presence of kinematicconstraints, to support data-driven simulation. Unlike other approaches, e.g., Fully-connected NeuralNetwork (FCNN) or Recurrent Neural Network (RNN)-based methods that are used to model the sys-tem states directly, the proposed approach embraces a Neural Ordinary Differential Equation (NODE)paradigm that models the derivatives of system states. A central part of the proposed methodology is itscapacity to learn the multibody system dynamics from prior physical knowledge and constraints com-bined with data inputs. This learning process is facilitated by a constrained optimization approach, whichensures that physical laws and system constraints are accounted for in the simulation process. The mod-els, data, and code for this work are available at

Keyphrases: constrained optimization, Multibody System Dynamics, Neural Ordinary Differential Equation, Physics-informed data-driven simulation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jingquan Wang and Shu Wang and Huzaifa Mustafa Unjhawala and Jinlong Wu and Dan Negrut},
  title = {Physics-Informed Data-Driven Simulation of Constrained Multibody Systems},
  howpublished = {EasyChair Preprint no. 13605},

  year = {EasyChair, 2024}}
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