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Medientyp: Sonstige Veröffentlichung; E-Artikel Titel: Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media Beteiligte: Guo, Hongwei [Verfasser:in]; Zhuang, Xiaoying [Verfasser:in]; Chen, Pengwan [Verfasser:in]; Alajlan, Naif [Verfasser:in]; Rabczuk, Timon [Verfasser:in] Erschienen: London : Springer, 2022 Erschienen in: Engineering with computers : an international journal for simulation-based engineering (2022), online first ; Engineering with computers : an international journal for simulation-based engineering Ausgabe: published Version Sprache: Englisch DOI: https://doi.org/10.15488/12961; https://doi.org/10.1007/s00366-021-01586-2 Schlagwörter: Neural architecture search ; Deep learning ; Sensitivity analysis ; Log-normally distributed ; Transfer learning ; Method of manufactured solutions ; Physics-informed ; Randomized spectral representation ; Hyper-parameter optimization algorithms ; Error estimation Entstehung: Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen. Beschreibung: We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost. © 2022, The Author(s). Zugangsstatus: Freier Zugang Rechte-/Nutzungshinweise: Namensnennung (CC BY)