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A Conditional Generative Model for End-to-End Stress Field Prediction of Composite Bolted Joints

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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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Three-dimensional contact stress analysis of a composite laminate with bolted joint
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Multi-scale neighborhood query graph convolutional network for multi-defect location in CFRP laminates
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An analytical model for the prediction of load distribution in highly torqued multi-bolt composite joints
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An analytical model for the prediction of through-thickness stiffness in tension-loaded composite bolted joints
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Lightweight design of carbon twill weave fabric composite body structure for electric vehicle
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Impact resistance and damage tolerance of fiber reinforced composites: A review
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An improved 3D model of composite bolted joints with detailed thread structure and progressive damage analysis of realistic tightening process
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A global interactive attention-based lightweight denoising network for locating internal defects of CFRP laminates
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A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition
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Deep transfer learning-based damage detection of composite structures by fusing monitoring data with physical mechanism
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Data-driven methods for stress field predictions in random heterogeneous materials
inferredcites →https://doi.org/10.1016/j.engstruct.2020.111228openalex-extracted
Bolt loosening detection using impedance based non-destructive method and probabilistic neural network technique with minimal training data
inferredcites →https://doi.org/10.1016/j.jcsr.2022.107755openalex-extracted
Conditional generative adversarial network for welding deformation field prediction of butt-welded plates
inferredcites →https://doi.org/10.1016/j.ymssp.2021.108067openalex-extracted
Reliability evaluation method of anti-loosening performance of bolted joints
inferredcites →https://doi.org/10.1016/s0952-1976(97)00007-9openalex-extracted
EADOCS: Conceptual design in three phases—An application to fibre reinforced composite panels
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Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework
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High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network
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Three-dimensional ESRGAN for super-resolution reconstruction of turbulent flows with tricubic interpolation-based transfer learning
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Three-dimensional tire-pavement contact stresses prediction by deep learning approach
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Mechanism and quantitative evaluation model of slip-induced loosening for bolted joints
inferredcites →https://doi.org/10.1109/acssc.2003.1292216openalex-extracted
Multiscale structural similarity for image quality assessment
inferredcites →https://doi.org/10.1109/iccv.2017.304openalex-extracted
Least Squares Generative Adversarial Networks
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Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
inferredcites →https://doi.org/10.1115/1.4049805openalex-extracted
StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction
inferredcites →https://doi.org/10.1155/2020/8811484openalex-extracted
Effect of Gap and Shims on the Strain and Stress State of the Composite-Aluminum Hybrid Bolted Structure
inferredcites →https://doi.org/10.1177/0731684417690615openalex-extracted
Mechanical and failure behavior of three-dimensional six-directional braided composites bolted joint
inferredcites →https://doi.org/10.1177/1687814016668445openalex-extracted
Calculation of the effective bearing contact radius for precision tightening of bolted joints
inferredcites →https://doi.org/10.1177/1687814021989506openalex-extracted
Influence of gap filling on mechanical properties of composite-aluminum single-lap single-bolt hybrid joints
inferredcites →https://doi.org/10.48550/arxiv.1411.1784openalex-extracted
Conditional Generative Adversarial Nets
inferredcites →https://doi.org/10.48550/arxiv.2004.08826openalex-extracted
DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2010.11348openalex-extracted
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
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