Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
<|reference_start|>Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy: Forward uncertainty quantification (UQ) for partial differential equations is a many-query task that requires a significant number of model evaluations. The objective of this work is to mitigate the computational cost of UQ for a 3D-1D multiscale computational model of microcirculation. To this purpose, we present a deep learning enhanced multi-fidelity Monte Carlo (DL-MFMC) method that integrates the information of a multiscale full-order model (FOM) with that coming from a deep learning enhanced non-intrusive projection-based reduced order model (ROM). The latter is constructed by leveraging on proper orthogonal decomposition (POD) and mesh-informed neural networks (previously developed by the authors and co-workers), integrating diverse architectures that approximate POD coefficients while introducing fine-scale corrections for the microstructures. The DL-MFMC approach provides a robust estimator of specific quantities of interest and their associated uncertainties, with optimal management of computational resources. In particular, the computational budget is efficiently divided between training and sampling, ensuring a reliable estimation process suitably exploiting the ROM speed-up. Here, we apply the DL-MFMC technique to accelerate the estimation of biophysical quantities regarding oxygen transfer and radiotherapy outcomes. Compared to classical Monte Carlo methods, the proposed approach shows remarkable speed-ups and a substantial reduction of the overall computational cost.<|reference_end|>
@article{vitullo2024deep,
    title={Deep learning enhanced cost-aware multi-fidelity uncertainty
  quantification of a computational model for radiotherapy},
    author={Piermario Vitullo, Nicola Rares Franco, Paolo Zunino},
    journal={arXiv preprint arXiv:2402.08494},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08494},
    primaryClass={math.NA cs.NA}
}