Computational mechanics enhanced by deep learning

Oishi, Atsuya; Yagawa, Genki

doi:10.1016/j.cma.2017.08.040

Cited by 168 publications

(120 citation statements)

References 92 publications

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“…At coarser scales, there are limited advances emerging for developing constitutive models [87,88,91], modeling hysteretic response [89], improving reduced order models [165,166], and even for optimizing numerical methods [167]. At still coarser scales, there is research to understand and model complex dynamical systems and to use ML methods for dimensionality reduction [168,169].…”

Section: Multiscale Mechanics and Properties Of Materials And Structuresmentioning

confidence: 99%

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Dimiduk

Holm

Niezgoda

2018

Integr Mater Manuf Innov

235

126

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The fields of machining learning and artificial intelligence are rapidly expanding, impacting nearly every technological aspect of society. Many thousands of published manuscripts report advances over the last 5 years or less. Yet materials and structures engineering practitioners are slow to engage with these advancements. Perhaps the recent advances that are driving other technical fields are not sufficiently distinguished from long-known informatics methods for materials, thereby masking their likely impact to the materials, processes, and structures engineering (MPSE). Alternatively, the diverse nature and limited availability of relevant materials data pose obstacles to machine-learning implementation. The glimpse captured in this overview is intended to draw focus to selected distinguishing advances, and to show that there are opportunities for these new technologies to have transformational impacts on MPSE. Further, there are opportunities for the MPSE fields to contribute understanding to the emerging machine-learning tools from a physics basis. We suggest that there is an immediate need to expand the use of these new tools throughout MPSE, and to begin the transformation of engineering education that is necessary for ongoing adoption of the methods.

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Section: Multiscale Mechanics and Properties Of Materials And Structuresmentioning

confidence: 99%

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Dimiduk

Holm

Niezgoda

2018

Integr Mater Manuf Innov

235

126

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“…Computer realisation of Bayesian inference theory belongs to the broad class of machine learning techniques. These methods are currently making a big impact in the computational mechanics community [207,208], as they go beyond mere data mining and machine learning. They also offer the possibility of creating and exploiting data-driven model-free simulations.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Biotribology of the ageing skin—Why we should care

et al. 2019

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Ageing of populations has emerged as one of the most pressing societal, economic and healthcare challenges currently facing most nations across the globe. The ageing process itself results in degradation of physiological functions and biophysical properties of organs and tissues, and more particularly those of the skin. Moreover, in both developed and emerging economies, population ageing parallels concerning increases in lifestyleassociated conditions such as Type 2 diabetes, obesity and skin cancers. When considered together, these demographic trends call for even greater urgency to find clinical and engineering solutions for the numerous age-related deficits in skin function. From a tribological perspective, detrimental alterations of skin bioph ysical properties with age have fundamental consequences on how one interacts with the body's inner and outer environments. This stems from the fact that, besides being the largest organ of the human body, and also nearly covering its entirety, the skin is a multifunctional interface which mediates these interactions. The aim of this paper is to present a focused review to discuss some of the consequences of skin ageing from the viewpoint of biotribology, and their implications on health, well -being and human activities. Current and future research questions/challenges associated with biotribology of the ageing ski n are outlined. They provide the background and motivation for identifying future lines of research that could be taken up by the biotribology and biophysics communities.

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“…In conjunction with machine learning techniques such as manifold learning [21] or neural networks [22], the recent studies [23][24][25] offer a new paradigm for data-driven computing for various applications such as design of materials [26]. There is a vast body of literature devoted to these subjects, including the recent developments based on nonlinear dimensionality reduction [24], nonlinear regression, deep learning [27][28][29], among others.…”

Section: Introductionmentioning

confidence: 99%

A physics-constrained data-driven approach based on locally convex reconstruction for noisy database

Chen

2020

Computer Methods in Applied Mechanics and Engineering

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Physics-constrained data-driven computing is a hybrid approach that integrates universal physical laws with data-based models of experimental data to enhance the scientific computing. A new data-driven simulation approach enriched with a locally convex reconstruction, termed the local convexity data-driven (LCDD) computing, is proposed to enhance accuracy and robustness against noise and outliers in data sets in the data-driven computing. In this approach, for a given state obtained by the physical simulation, the corresponding optimum experimental solution is sought by projecting the state onto the associated local convex manifold reconstructed based on the nearest experimental data. This learning process of local data structure is less sensitivity to noisy data and consequently yields better accuracy. A penalty relaxation is also introduced to recast the local learning solver in the context of non-negative least squares that can be solved effectively. The reproducing kernel approximation with stabilized nodal integration are employed for the solution of the physical manifold to allow reduced stress-strain data at the discrete points for enhanced effectiveness in the LCDD learning solver. Due to the inherent manifold learning properties, LCDD performs well for high-dimensional data sets that are relatively sparse in real-world engineering applications. Numerical tests demonstrated that LCDD enhances nearly one order of accuracy compared to the standard distance-minimization data-driven scheme when dealing with noisy database, and a linear exactness is achieved when local stress-strain relation is linear.

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Computational mechanics enhanced by deep learning

Cited by 168 publications

References 92 publications

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Biotribology of the ageing skin—Why we should care

A physics-constrained data-driven approach based on locally convex reconstruction for noisy database

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