Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation

Kim, Suhan; Shin, Hyunseong

doi:10.1016/j.cma.2023.116131

Cited by 13 publications

(1 citation statement)

References 68 publications

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“…At the macro scale, the distance minimization data-driven method is used to directly search the material database and simulate structural behavior without using a constitutive model. Kim and Shin [82] proposed a deep learning framework for multi-scale finite element analysis (FE 2 ). To overcome the low concurrency and efficiency caused by the repeated analysis of various macro integration points in the classical FE 2 method, distance minimization data-driven computational mechanics is used for FE 2 analysis.…”

Section: Research On Data Driven Multiscale Computingmentioning

confidence: 99%

Review of multi‐scale mechanical behavior research on composite solid propellants based on data‐driven approach

Yuan,

Qiang,

Wang

et al. 2024

Propellants Explo Pyrotec

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Composite solid propellant is a kind of viscoelastic composite with high filling ratio and multi‐scale composition characteristics, and its macroscopic mechanical properties strongly depend on the microstructure of the propellant materia. However, with the increasing complexity of composition, structure and properties of composite solid propellants, the traditional research paradigm based on experimental observation, theoretical modeling and numerical simulation has encountered new scientific challenges and technical bottlenecks in the mechanical behavior analysis, charge design and manufacturing of composite solid propellants. Among them, the problems such as insufficient experimental observation, lack of theoretical model, limited numerical analysis and difficult verification of results restrict the development of composite solid propellants in future‐oriented engineering applications to a certain extent. The data‐driven computational mechanics method can directly establish complex relationships between variables from high‐dimensional and high‐throughput data, which can capture trends that are difficult to be found by traditional mechanics research methods, and has inherent advantages in the analysis, prediction and optimization of complex systems. This paper mainly reviews and evaluates the research of neural network based modeling, model‐free data‐driven calculation and data‐driven multi‐scale calculation, which provides the correct direction for the subsequent research of multi‐scale mechanical behavior of composite solid propellants based on data‐driven.

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Section: Research On Data Driven Multiscale Computingmentioning

confidence: 99%

Review of multi‐scale mechanical behavior research on composite solid propellants based on data‐driven approach

Yuan,

Qiang,

Wang

et al. 2024

Propellants Explo Pyrotec

View full text Add to dashboard Cite

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High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

Atangana Njock,

Yin,

Zhang

2024

Num Anal Meth Geomechanics

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Contemporary geoengineering challenges grapple with the plateauing of both existing algorithms and their depth of insights, a phenomenon exacerbated by the scarcity of high‐fidelity data. Although existing solutions such as Monte‐Carlo method can generate abundant data, they are not sufficiently robust for ensuring the high fidelity of data. This study proposes a novel data augmentation framework that combines statistical and machine learning methods to generate high‐fidelity synthetic data, which closely align with field data in terms of the statistical and empirical attributes. The innovations of the proposed approach lie in the integration of Copulas theory for data generation, a developed geo‐regression anomaly detection (GRAD) for adjusting data attributes, and an evolutionary polynomial regression for data consistency enforcement. The multilayer perceptron (MLP) and a wide‐and‐deep (WaD) networks are applied to assess the effectiveness of high‐fidelity data augmentation using jet grouting data. The outcomes reveal the robustness of the synthetic data generation framework, achieving satisfactory fidelity in both empirical and statistical attributes. The proposed data augmentation improved the R2 and MAE achieved by MLP and WaD up to 28.37% under data fractions ranging from 0.2 to 1. MLP and WaD yielded comparable results in terms of accuracy and generalization ability across various augmented fractions. This indicates that the accuracy of synthetic data plays a pivotal role, suggesting improving data quality can be highly effective in boosting performance, regardless of the model complexity. This study contributes valuable insights to addressing the challenges of scare high‐fidelity data in geoengineering.

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Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review

Nath,

Ankit,

Neog

et al. 2024

Arch Computat Methods Eng

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Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation

Cited by 13 publications

References 68 publications

Review of multi‐scale mechanical behavior research on composite solid propellants based on data‐driven approach

Review of multi‐scale mechanical behavior research on composite solid propellants based on data‐driven approach

High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review

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