Abstract
Xingyuan Zheng, Yumei Hu, Xiangning Zhang, Robert G. Parker
Abstract
Authors
Institutions
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Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
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Extraction of mechanical properties of materials through deep learning from instrumented indentation
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Mining the correlations between optical micrographs and mechanical properties of cold-rolled HSLA steels using machine learning approaches
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A machine learning-based method to design modular metamaterials
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Bayesian machine learning in metamaterial design: fragile becomes supercompressible
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Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
10.1016/j.cma.2022.115027 · doi-reference
Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning
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Thermodynamics-based artificial neural networks for constitutive modeling
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A critical review on FE-based methods for mesh stiffness estimation in spur gears
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An extensive review of vibration modelling of rolling element bearings with localised and extended defects
10.1016/j.jsv.2015.04.037 · doi-reference
Finite element modeling and parametric study of an automotive v-belt pulley for durability improvement
10.1007/s12541-015-0201-3 · doi-reference
Transient and steady-state dynamic finite element modeling of belt-drives
10.1115/1.1513793 · doi-reference
On a perturbation method for the analysis of unsteady belt-drive operation
10.1115/1.1940660 · doi-reference