Predicting Finite Element Simulation output using Machine Learning: A study to understand the potential of Graph Neural Networks in predicting vehicle occupant pre-crash simulation kinematics
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Biomedical engineering (MPBME), MSc
Publicerad
2022
Författare
Fichera, Chiara Rosanna
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Each year around 1.35 million people die in traffic crashes and many more get injured. Human Body Models have become a popular tool to understand and prevent
injuries and fatalities as they simulates the human body response during crash sce narios.
With improvements in the accuracy and bio-fidelity of Human Body Models, the
demand for computational resources is increased up to requiring days for a pre single crash simulation. Therefore, Dimensionality Reduction methods can be used
to create a surrogate that is a smaller representation of the model. The reduced
representation translated to a latent space that still retains the complexity and the
features of the simulation. To build the surrogate, supervised learning tasks have
been implemented on the reduced dimension to map the simulation to its parameters.
To create a compressed version of the input simulation, Graph Neural Networks
have been considered since they incorporate the geometrical structure of the model.
The Graph Neural Network has been compared to an equivalent Convolutional Neu ral Network architecture and to a Principal Component Analysis. Random Forest,
Gradient Boosting, and XGBoost were chosen and compared to build the surrogate
model. Due to computational limitations, and since pre-crash simulations are al ready time-consuming, a simplified Hybrid III dummy model was chosen in place of
a full Human Body Model.
For all Dimensionality Reduction methods, the accuracy of the reconstruction im proves by increasing both the number of samples and the latent space size. Principal
Component Analysis shows a better performance in terms of lower errors compared
to Graph Neural Network. Moreover, the Graph Neural Network structure is compa rable to an equivalent Convolutional Neural Network architecture in term of perfor mance. Random Forest and Gradient boosting proved to be better than XGBoost.
Principal Component Analysis requires less computational time and resources.
Although Graph Neural Network was outperformed in this study, further improve ments in the development of the method may still have potential.
Beskrivning
Ämne/nyckelord
HBM , Dimensionality Reduction , Graph Neural network , Principal Component Analysis , Machine Learning