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- 01JFTXRZBXT6QP8T85MB0N126Q classification A1.
- 01JFTXRZBXT6QP8T85MB0N126Q date "2025".
- 01JFTXRZBXT6QP8T85MB0N126Q language "eng".
- 01JFTXRZBXT6QP8T85MB0N126Q type journalArticle.
- 01JFTXRZBXT6QP8T85MB0N126Q hasPart 01JFTXX2316WS9GJH87B82XBY1.pdf.
- 01JFTXRZBXT6QP8T85MB0N126Q hasPart urn:uuid:7044b444-0dd4-4fa3-8d34-750ae7151579.
- 01JFTXRZBXT6QP8T85MB0N126Q subject "Technology and Engineering".
- 01JFTXRZBXT6QP8T85MB0N126Q doi "10.1016/j.cma.2024.117603".
- 01JFTXRZBXT6QP8T85MB0N126Q issn "0045-7825".
- 01JFTXRZBXT6QP8T85MB0N126Q issn "1879-2138".
- 01JFTXRZBXT6QP8T85MB0N126Q volume "435".
- 01JFTXRZBXT6QP8T85MB0N126Q abstract "Data-based uncertainty quantification plays a significant role in the design of various patterns of new materials and structures. However, significant challenges remain due to missing data, inherent uncertainties, and incomplete material properties arising from the manufacturing process. In this paper, we quantitatively investigate the uncertainty in the probability of the mechanical response of bio-inspired porous structures, specifically focusing on a RotTPMS plate as input data in the current study. Practicality, datasets can be collected from arbitrary material sources such as open-access libraries, experimental data, or numerical simulation results. Initially, machine learning models were utilized to handle incomplete data on metal material properties through imputation. The imputation methods used for filling in the data include MEAN, KNN, MICE, GAIN, and MISSFOREST. The results showed that the MISSFOREST method was the most accurate, with the lowest MAPE values of 3.19% for E s , 0.66% for v s , and 2.6% for p s . Concurrently, we introduce an efficient BNN-pSGLD model, which employs Bayesian neural networks with the preconditioned stochastic gradient Langevin dynamics method for sampling and optimization, aimed at data-driven uncertainty quantification. A data-driven computational framework, named Material-UQ, is proposed to probabilistically predict the mechanical response of various structures, accounting for uncertainties in material properties. The BNN-pSGLD model achieves higher R 2 performance than conventional machine learning models such as ANNs, Decision Trees, and Random Forests. Additionally, to assist designers in accurately predicting and managing uncertainties in material and structural design, detailed discussions and deep explanations of the uncertainty in the present approach are also conducted.".
- 01JFTXRZBXT6QP8T85MB0N126Q author 2223D1E4-F0EE-11E1-A9DE-61C894A0A6B4.
- 01JFTXRZBXT6QP8T85MB0N126Q author urn:uuid:1f8417cc-954e-4e6f-8105-021ba250b24e.
- 01JFTXRZBXT6QP8T85MB0N126Q author urn:uuid:20b648ac-f2a8-458c-b596-400b133760d4.
- 01JFTXRZBXT6QP8T85MB0N126Q author urn:uuid:3c622b2c-ee99-47bb-9b2e-c5498042108b.
- 01JFTXRZBXT6QP8T85MB0N126Q author urn:uuid:f99697ca-399a-4e45-b6bb-f8f7156160ed.
- 01JFTXRZBXT6QP8T85MB0N126Q dateCreated "2024-12-23T23:31:28Z".
- 01JFTXRZBXT6QP8T85MB0N126Q dateModified "2025-01-30T09:54:05Z".
- 01JFTXRZBXT6QP8T85MB0N126Q name "A data-driven uncertainty quantification framework in probabilistic bio-inspired porous materials (Material-UQ) : an investigation for RotTMPS plates".
- 01JFTXRZBXT6QP8T85MB0N126Q pagination urn:uuid:1b9c336d-9008-494f-aabf-5a6a804a8803.
- 01JFTXRZBXT6QP8T85MB0N126Q sameAs LU-01JFTXRZBXT6QP8T85MB0N126Q.
- 01JFTXRZBXT6QP8T85MB0N126Q sourceOrganization urn:uuid:12e2951b-f976-4e11-9ac9-7f624d4dbe7e.
- 01JFTXRZBXT6QP8T85MB0N126Q type A1.