A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

By Miguel A. Bessa, Ramin Bostanabad, Zeliang Liu, A. Hu, Daniel W. Apley, C. Brinson, Wei Chen, Wing Kam Liu. 2017

525 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/57248a0f4b04564e546ced78d8890c59a9b62ada

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In Materials Chemistry

In Mechanics of Materials

Citations in the corpus

Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design (2019, background): “For emerging material systems, in particular, large datasets have been successfully leveraged to design heterogeneous materials [4 – 8] and mechanical metamaterials [9 – 12].”
Deep convolutional neural networks for eigenvalue problems in mechanics (2018, background): “There has admittedly been a recent push towards purely data driven mechanics computing [11, 12, 13, 14, 15].”
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, background): “For a more complete description of the case study, see [281].”
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity (2020, unknown): “…when human interpretation capabilities become restrictive (Kirchdoerfer and Ortiz, 2016; Eggersmann, Kirchdoerfer, Reese, Stainier, and Ortiz, 2019; He and Chen, 2019; Stoffel, Bamer, and Markert, 2019; Bessa, Bostanabad, Liu, Hu, Apley, Brinson, Chen, and Liu, 2017; Liu, Kafka, Yu, and Liu, 2018).”
Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials (2018, background): “toughness) within a computational data-driven framework of material design under uncertainty [28].”
A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, methodology): “[25] M.”
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning (2018, methodology): “Data-driven models such as Le et al. [2015], Bessa et al. [2017], Versino et al. [2017], Kafka et al. [2018] and Wulﬁnghoff et al. [2018] attempted to overcome this issue via supervised machine learning (e.g. neural network [Leﬁk and Schreﬂer, 2002], symbolic regression model [Versino et al.,…”