Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
<|reference_start|>Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids: Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is still a severely under-invested problem, especially when input-output relationships are non-linear. To handle this problem, the present work introduces an innovative approach that combines autoencoder deep neural networks with the probabilistic regression capabilities of Gaussian processes. The autoencoder provides a low-dimensional representation of the solution space, while the Gaussian process is a Bayesian method that provides a probabilistic mapping between the low-dimensional inputs and outputs. We validate the proposed framework for its application to surrogate modeling of non-linear finite element simulations. Our findings highlight that the proposed framework is computationally efficient as well as accurate in predicting non-linear deformations of solid bodies subjected to external forces, all the while providing insightful uncertainty assessments.<|reference_end|>
@article{deshpande2024gaussian,
    title={Gaussian process regression + deep neural network autoencoder for
  probabilistic surrogate modeling in nonlinear mechanics of solids},
    author={Saurabh Deshpande, Hussein Rappel, Mark Hobbs, St'ephane P.A. Bordas,
  Jakub Lengiewicz},
    journal={arXiv preprint arXiv:2407.10732},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10732},
    primaryClass={cs.CE}
}