Correlations in the shear flow of athermal amorphous solids: a principal component analysis

Ruscher, Céline; Rottler, Jörg

doi:10.1088/1742-5468/ab3aea

Cited by 6 publications

(8 citation statements)

References 31 publications

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“…Here, our 2D images are composed of total-strain features in columns, while each row contains a different applied strain. For clarity, in this way, the images have 30 rows but 2 18 or 2 16 columns. We focus solely on the features of ψ V which capture localization along the loading direction.…”

Section: Resultsmentioning

confidence: 99%

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Direct detection of plasticity onset through total-strain profile evolution

Papanikolaou

Alava

2021

Phys. Rev. Materials

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Plasticity in solids is dependent on microstructural history, temperature, and loading rate, and sampledependent knowledge of yield points in structural materials adds reliability to mechanical behavior. Yielding is commonly measured through controlled mechanical testing, in ways that either distinguish elastic (stress) from total deformation measurements or identify plastic slip contributions. In this paper, we show that yielding can be unraveled through statistical analysis of total-strain fluctuations during the evolution sequence of profiles, measured in situ, through digital image correlation. We demonstrate two distinct ways of quantifying yield locations in widely applicable crystal plasticity models for polycrystalline solids, using either principal component analysis or discrete wavelet transforms. We test and compare these approaches for synthetic data of polycrystals and a variety of yielding responses through changes in applied loading rates and strain-rate sensitivity exponents.

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Section: Resultsmentioning

confidence: 99%

“…However, the absence of detailed knowledge may lead to overfitting artifacts and unsuccessful machine learning training. In this context, the use of unsupervised machine learning through principal component analysis (PCA) has been insightful [8,9,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Direct detection of plasticity onset through total-strain profile evolution

Papanikolaou

Alava

2021

Phys. Rev. Materials

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“…The yield at this level of disorder comes with an opening of a pseudogap indicating the development of the global connectivity of the energy landscape [72,92]. The increase of the applied strain γ beyond the critical strain, decreases the pseudogap exponent again, bringing it to a plateau with θ ∼ 0, characterizing the stationary regime [77]. We can conjecture that this as a signature of the emerging depinning scaling.…”

Section: Excitation Spectramentioning

confidence: 87%

“…[32]). In fact, the authors of these studies have already linked such regimes with both dislocation jamming and self-induced glassiness [33,68,76,77].…”

Section: Avalanche Statisticsmentioning

confidence: 99%

Variety of scaling behaviors in nanocrystalline plasticity

Zhang

Salman²,

Weiss

et al. 2020

Phys. Rev. E

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We address the question of why larger, high-symmetry crystals are mostly weak, ductile, and statistically subcritical, while smaller crystals with the same symmetry are strong, brittle and supercritical. We link it to another question of why intermittent elasto-plastic deformation of submicron crystals features highly unusual size sensitivity of scaling exponents. We use a minimal integer-valued automaton model of crystal plasticity to show that with growing variance of quenched disorder, which can serve in this case as a proxy for increasing size, submicron crystals undergo a crossover from spin-glass marginality to criticality characterizing the second order brittle-to-ductile (BD) transition. We argue that this crossover is behind the nonuniversality of scaling exponents observed in physical and numerical experiments. The nonuniversality emerges only if the quenched disorder is elastically incompatible, and it disappears if the disorder is compatible.

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“…in the XY model on frustrated triangular and union jack lattices (Wang and Zhai, 2017). PCA was also used to classify dislocation patterns in crystals (Papanikolaou et al , 2017; Wang and Zhai, 2018), and to find correlations in the shear flow of athermal amorphous solids (Ruscher and Rottler, 2018). PCA is widely employed in biological physics when working with high-dimensional data.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

A high-bias, low-variance introduction to Machine Learning for physicists

et al. 2019

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Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.

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Correlations in the shear flow of athermal amorphous solids: a principal component analysis

Cited by 6 publications

References 31 publications

Direct detection of plasticity onset through total-strain profile evolution

Direct detection of plasticity onset through total-strain profile evolution

Variety of scaling behaviors in nanocrystalline plasticity

A high-bias, low-variance introduction to Machine Learning for physicists

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