On the public citation graph

Data-Driven Lay-Up Design of a Type Ⅳ Hydrogen Storage Vessel Based on Physics-Constrained Generative Adversarial Networks (Pcgans)

https://doi.org/10.2139/ssrn.4799748
0 signed · 50 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/er.2983openalex-extracted
Optimal design of a composite laminate hydrogen storage vessel
inferredcites →https://doi.org/10.1007/978-3-658-40442-0_9openalex-extracted
Generative Adversarial Nets
inferredcites →https://doi.org/10.1007/s00521-019-04280-zopenalex-extracted
Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: the advantage of more elementary-level analyses
inferredcites →https://doi.org/10.1007/s11042-019-08453-9openalex-extracted
Dropout vs. batch normalization: an empirical study of their impact to deep learning
inferredcites →https://doi.org/10.1016/j.asoc.2022.109217openalex-extracted
Physics-informed deep learning: A promising technique for system reliability assessment
inferredcites →https://doi.org/10.1016/j.commatsci.2018.05.014openalex-extracted
Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
inferredcites →https://doi.org/10.1016/j.compositesa.2019.02.002openalex-extracted
Vibration-based delamination detection in curved composite plates
inferredcites →https://doi.org/10.1016/j.compscitech.2020.108034openalex-extracted
Application of Artificial Neural Networks to predict fibre orientation in long fibre compression moulded composite materials
inferredcites →https://doi.org/10.1016/j.compscitech.2021.109154openalex-extracted
A deep learning-based composite design strategy for efficient selection of material and layup sequences from a given database
inferredcites →https://doi.org/10.1016/j.compstruc.2008.03.001openalex-extracted
Improved genetic algorithm for multidisciplinary optimization of composite laminates
inferredcites →https://doi.org/10.1016/j.compstruct.2020.111915openalex-extracted
Design of a 70 MPa type IV hydrogen storage vessel using accurate modeling techniques for dome thickness prediction
inferredcites →https://doi.org/10.1016/j.compstruct.2020.112407openalex-extracted
Theory-guided machine learning for damage characterization of composites
inferredcites →https://doi.org/10.1016/j.compstruct.2020.112654openalex-extracted
Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network
inferredcites →https://doi.org/10.1016/j.compstruct.2020.113046openalex-extracted
Progressive damage analysis for multiscale modelling of composite pressure vessels based on Puck failure criterion
inferredcites →https://doi.org/10.1016/j.compstruct.2020.113387openalex-extracted
Investigation on failure behaviors of 70 MPa Type IV carbon fiber overwound hydrogen storage vessels
inferredcites →https://doi.org/10.1016/j.conbuildmat.2023.130704openalex-extracted
Reconstruction of the meso-scale concrete model using a deep convolutional generative adversarial network (DCGAN)
inferredcites →https://doi.org/10.1016/j.engfailanal.2021.105851openalex-extracted
Development of Abaqus WCM plugin for progressive failure analysis of type IV composite pressure vessels based on Puck failure criterion
inferredcites →https://doi.org/10.1016/j.ijhydene.2009.05.008openalex-extracted
Optimal design of high pressure hydrogen storage vessel using an adaptive genetic algorithm
inferredcites →https://doi.org/10.1016/j.ijhydene.2013.07.016openalex-extracted
Optimization of carbon fiber usage in Type 4 hydrogen storage tanks for fuel cell automobiles
inferredcites →https://doi.org/10.1016/j.ijhydene.2015.05.061openalex-extracted
A progressive failure analysis of a 700-bar type IV hydrogen composite pressure vessel
inferredcites →https://doi.org/10.1016/j.ijhydene.2015.05.126openalex-extracted
700 bar type IV high pressure hydrogen storage vessel burst – Simulation and experimental validation
inferredcites →https://doi.org/10.1016/j.ijhydene.2015.06.121openalex-extracted
Modeling, parameters identification and experimental validation of composite materials behavior law used in 700 bar type IV hydrogen high pressure storage vessel
inferredcites →https://doi.org/10.1016/j.ijhydene.2015.06.156openalex-extracted
Optimisation of 700 bar type IV hydrogen pressure vessel considering composite damage and dome multi-sequencing
inferredcites →https://doi.org/10.1016/j.ijhydene.2017.03.032openalex-extracted
Optimization of Type 4 composite pressure vessels using genetic algorithms and simulated annealing
inferredcites →https://doi.org/10.1016/j.ijhydene.2017.08.123openalex-extracted
Performance assessment of 700-bar compressed hydrogen storage for light duty fuel cell vehicles
inferredcites →https://doi.org/10.1016/j.ijhydene.2019.08.001openalex-extracted
A literature review of failure prediction and analysis methods for composite high-pressure hydrogen storage tanks
inferredcites →https://doi.org/10.1016/j.ijhydene.2020.06.269openalex-extracted
Theoretical analysis of design of filament wound type 3 composite cylinder for the storage of compressed hydrogen gas
inferredcites →https://doi.org/10.1016/j.ijhydene.2021.04.186openalex-extracted
Simulation and burst validation of 70 MPa type IV hydrogen storage vessel with dome reinforcement
inferredcites →https://doi.org/10.1016/j.ijhydene.2021.05.176openalex-extracted
Numerical assestments of maximum depressurisation rate for polymer materials under high-pressure hydrogen
inferredcites →https://doi.org/10.1016/j.ijhydene.2021.06.177openalex-extracted
Fatigue life prediction and verification of high-pressure hydrogen storage vessel
inferredcites →https://doi.org/10.1016/j.ijhydene.2022.09.028openalex-extracted
Review on optimization design, failure analysis and non-destructive testing of composite hydrogen storage vessel
inferredcites →https://doi.org/10.1016/j.ijpvp.2018.08.008openalex-extracted
The applicability and implementation of the discrete Big Bang-Big Crunch optimisation technique for discontinuous objective function in multi-material laminated composite pressure vessels
inferredcites →https://doi.org/10.1016/j.ijpvp.2022.104779openalex-extracted
Application of tabular data synthesis using generative adversarial networks on machine learning-based multiaxial fatigue life prediction
inferredcites →https://doi.org/10.1016/j.ijpvp.2023.104986openalex-extracted
Numerical study on the influence of liner geometry and lay-up sequence on the fatigue life of composite hydrogen storage vessel
inferredcites →https://doi.org/10.1016/j.matpr.2019.12.336openalex-extracted
Neural network for mechanical property estimation of multilayered laminate composite
inferredcites →https://doi.org/10.1021/acsami.1c01044openalex-extracted
Computational Discovery of New 2D Materials Using Deep Learning Generative Models
inferredcites →https://doi.org/10.1115/1.4049644openalex-extracted
A Comprehensive Study on Burst Pressure Performance of Aluminum Liner for Hydrogen Storage Vessels
inferredcites →https://doi.org/10.1126/sciadv.aaz4169openalex-extracted
Designing complex architectured materials with generative adversarial networks
inferredcites →https://doi.org/10.1126/sciadv.abd7416openalex-extracted
Deep learning model to predict complex stress and strain fields in hierarchical composites
inferredcites →https://doi.org/10.1177/0021998319870588openalex-extracted
Investigation of interlayer hybridization effect on burst pressure performance of composite overwrapped pressure vessels with load-sharing metallic liner
inferredcites →https://doi.org/10.1177/00219983211033568openalex-extracted
Development and analysis of composite overwrapped pressure vessels for hydrogen storage
inferredcites →https://doi.org/10.1177/1475921719899334openalex-extracted
Online structural state assessment for aerospace composite structures using an acousto-ultrasonics-based multi-damage index identification approach
inferredcites →https://doi.org/10.3390/ma13173881openalex-extracted
Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force
inferredcites →https://doi.org/10.3390/polym14050902openalex-extracted
Thickness-Prediction Method Involving Tow Redistribution for the Dome of Composite Hydrogen Storage Vessels
inferredcites →https://doi.org/10.3390/wevj12030130openalex-extracted
Review of the Hydrogen Permeability of the Liner Material of Type IV On-Board Hydrogen Storage Tank
inferredcites →https://doi.org/10.4271/2015-01-1169openalex-extracted
Development of High-Pressure Hydrogen Storage System for the Toyota “Mirai”
inferredcites →https://doi.org/10.48550/arxiv.1411.1784openalex-extracted
Conditional Generative Adversarial Nets
inferredcites →https://doi.org/10.48550/arxiv.1511.06434openalex-extracted
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
inferredcites →https://doi.org/10.48550/arxiv.1701.00160openalex-extracted
NIPS 2016 Tutorial: Generative Adversarial Networks
inferredcites →https://doi.org/10.6052/1000-0992-21-019openalex-extracted
Application of artificial intelligence in composite materials
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.