On the public citation graph

Efficient Prediction Method for 3D Mesoscopic Stress Fields in Composite Solid Propellants Based on Multi-slice Input

https://doi.org/10.2139/ssrn.6066905
0 signed · 25 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/(sici)1097-0207(19990330)44:9<1267::aid-nme486>3.0.co;2-7openalex-snapshot
Finite-deformation irreversible cohesive elements for three-dimensional crack-propagation analysis
inferredcites →https://doi.org/10.1007/s11668-020-01056-9openalex-snapshot
Finite Element Analysis of Adhesive Failure of Solid Composite Propellant Multi-material Structure for Underwater Intelligent Equipment
inferredcites →https://doi.org/10.1016/0045-7949(91)90428-oopenalex-snapshot
A two-dimensional automatic mesh generator for finite element analysis for random composites
inferredcites →https://doi.org/10.1016/j.cis.2022.102692openalex-snapshot
Random sequential adsorption: An efficient tool for investigating the deposition of macromolecules and colloidal particles
inferredcites →https://doi.org/10.1016/j.cma.2006.06.008openalex-snapshot
Finite element formulation for modeling particle debonding in reinforced elastomers subjected to finite deformations
inferredcites →https://doi.org/10.1016/j.cma.2024.116816openalex-snapshot
Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network
inferredcites →https://doi.org/10.1016/j.cma.2024.117486openalex-snapshot
Bayesian neural networks for predicting uncertainty in full-field material response
inferredcites →https://doi.org/10.1016/j.compositesa.2024.108618openalex-snapshot
Integrated convolutional and graph neural networks for predicting mechanical fields in composite microstructures
inferredcites →https://doi.org/10.1016/j.compositesb.2020.107798openalex-snapshot
Effect of microstructure on mechanical properties of net-structured TiBw/TA15 composite subjected to hot plastic deformation
inferredcites →https://doi.org/10.1016/j.compositesb.2022.109879openalex-snapshot
Stress field prediction in fiber-reinforced composite materials using a deep learning approach
inferredcites →https://doi.org/10.1016/j.compscitech.2006.06.017openalex-snapshot
Multiscale modeling of solid propellants: From particle packing to failure
inferredcites →https://doi.org/10.1016/j.compscitech.2022.109735openalex-snapshot
Mesh/membrane composite with superior mechanical performance: A deep learning-based design
inferredcites →https://doi.org/10.1016/j.compscitech.2025.111198openalex-snapshot
Deep learning approach for predicting multi-component stress fields in fiber-reinforced composites under different load paths
inferredcites →https://doi.org/10.1016/j.compscitech.2025.111357openalex-snapshot
A two-stage deep learning framework for predicting crack patterns and mechanical properties of unidirectional composites with void defects
inferredcites →https://doi.org/10.1016/j.compstruct.2021.113957openalex-snapshot
Difference-based deep learning framework for stress predictions in heterogeneous media
inferredcites →https://doi.org/10.1016/j.dt.2022.09.008openalex-snapshot
Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites
inferredcites →https://doi.org/10.1016/j.engappai.2024.108622openalex-snapshot
A neural network transformer model for composite microstructure homogenization
inferredcites →https://doi.org/10.1016/j.engfracmech.2024.110675openalex-snapshot
A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties
inferredcites →https://doi.org/10.1016/j.jmps.2021.104506openalex-snapshot
End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures
inferredcites →https://doi.org/10.1016/j.matdes.2020.108509openalex-snapshot
Prediction of composite microstructure stress-strain curves using convolutional neural networks
inferredcites →https://doi.org/10.1016/j.matdes.2024.113261openalex-snapshot
Damage analysis of solid propellants with default defects based on macro-microscopic approach
inferredcites →https://doi.org/10.1016/j.mechmat.2006.08.008openalex-snapshot
Cohesive modeling of dewetting in particulate composites: micromechanics vs. multiscale finite element analysis
inferredcites →https://doi.org/10.1016/j.mechmat.2020.103408openalex-snapshot
Relationship between local damage and macroscopic response of soft materials highly reinforced by monodispersed particles
inferredcites →https://doi.org/10.1016/j.mechmat.2023.104709openalex-snapshot
Accelerated multiscale mechanics modeling in a deep learning framework
inferredcites →https://doi.org/10.3390/ma14216657openalex-snapshot
Solid Propellant Formulations: A Review of Recent Progress and Utilized Components
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.