Investigation of strain rate behavior of aluminium and AA2024 using crystal plasticity

Singh, Lakhwinder; Vohra, Sanjay; Sharma, Manu

doi:10.1016/j.matpr.2021.10.232

Cited by 2 publications

(1 citation statement)

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“…In this study, we focus on the effect of strain-rate on the stress-strain curve with various average grain sizes. The effect of strain-rate has been experimentally validated in Benzing et al [49], and some numerical studies using crystal plasticity finite element model have been done as well, such as those found in Singh et al [50]. The inputs of the GP are the average grain size, the strain-rate, the strain, i.e.…”

Section: Resultsmentioning

confidence: 97%

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Tran

Maupin

Rodgers

2022

Journal of Computing and Information Science in Engineering

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Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce or when the dimensionality is high, and monotonicity is supported by strong physical evidence.

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Section: Resultsmentioning

confidence: 97%