Machine learning-aided stochastic static analysis of functionally graded porous plates
- Publisher:
- Elsevier
- Publication Type:
- Chapter
- Citation:
- Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures, 2023, pp. 271-292
- Issue Date:
- 2023-01-01
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22612009_14128504670005671.pdf | Published version | 2.12 MB |
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A machine learning-aided stochastic framework is introduced for the static analysis (MLA-SSA) of functionally graded porous (FGP) structures involving uncertainties. The proposed framework solves the random static analysis problems with uncertain systematic inputs. Various types of material properties can be modeled as spatially dependent uncertainties as random fields. To effectively solve the stochastic static problems, a novel machine learning technique, namely the extended support vector regression is introduced, along with a typical algorithm named the neural network as comparison. Through the MLA-SSA framework, the statistical moments, the probability density function and the cumulative density function of random deflections can be acquired in an efficient manner. Detailed numerical investigations have been implemented to illustrate the accuracy, efficiency, and applicability of the MLA-SSA framework for FGP structures.
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