Multi-Hierarchical Surrogate Learning for Structural Dynamics of Automotive Crashworthiness Using Graph Convolutional Neural Networks

arXiv:2402.09234v1 Announce Type: new
Abstract: Crash simulations play an essential role in improving vehicle safety, design optimization, and injury risk estimation. Unfortunately, numerical solutions of such problems using state-of-the-art high-fidelity models require significant computational effort. Conventional data-driven surrogate modeling approaches create low-dimensional embeddings for evolving the dynamics in order to circumvent this computational effort. Most approaches directly operate on high-resolution data obtained from numerical discretization, which is both costly and complicated for mapping the flow of information over large spatial …

abstract art arxiv automotive computational convolutional neural networks cs.lg data data-driven design dynamics embeddings fidelity graph hierarchical low math.ds modeling networks neural networks numerical optimization risk role safety simulations solutions state type