Deep learning-based high-throughput mesomechanical analysis framework for particulate composites
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Composite Material Mechanics · Machine Learning in Materials Science · Model Reduction and Neural Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
Show source-provided references
1. Computational study on the electrical conductivity of hybrid composites under mechanical deformation
2. A comparative study of the cryogenic performance of CFRP composites with polyethersulfone/epoxy blends and electrospun polyethersulfone interleaves
3. Mesomechanical modeling of asphalt concrete considering full aggregate size range and conjunctive shell mechanism of interface transition zones10.1016/j.powtec.2024.120155 ↗
4. A polyconvex transversely-isotropic invariant-based formulation for electro-mechanics: stability, minimisers and computational implementation10.1016/j.cma.2022.115695 ↗
5. Plastic-Damage model for cyclic loading. Use of the Rule of Mixtures in homogeneous materials10.1016/j.cma.2025.118033 ↗
6. Microstructure dependent transverse strength criterion for UD-CFRP composites via computational micromechanics and machine learning10.1016/j.compscitech.2024.110551 ↗
7. A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality10.1016/j.cma.2017.03.037 ↗
8. The high-throughput highway to computational materials design10.1038/nmat3568 ↗
9. Automated homogenization-based fracture analysis: effects of SVE size and boundary condition
10. Mesoscopic insight into the damage mechanism for the static preload effect on dynamic tensile strength of concrete10.1061/(ASCE)MT.1943-5533.0002602 ↗
11. Data-mining framework integrating 3D random aggregate method and finite-element method for mesoscopic simulation of asphalt concrete
12. A comprehensive framework for 3D mesoscopic modelling of concrete: Innovations in aggregate mixing, placement domain shapes, and aggregate volume fraction adaptability10.1016/j.conbuildmat.2025.140894 ↗
13. A finite element analysis method for random fiber-aggregate 3D mesoscale concrete based on the crack bridging law
14. An improved random sequential absorption method for thermal conductivity study of diamond-reinforced composites10.1016/j.diamond.2025.112324 ↗
15. Investigating dimensional effects on predicting burning rates of heterogeneous solid propellants10.2514/1.J058631 ↗
16. Generation and numerical analysis of random aggregate structures in recycled concrete aggregate systems10.1061/(ASCE)MT.1943-5533.0003113 ↗
17. A two-dimensional random aggregate structure generation method: Determining effective thermo-mechanical properties of asphalt concrete10.1016/j.mechmat.2020.103510 ↗
18. Estimation of Local Strain Fields in Two-Phase Elastic Composite Materials Using UNet-Based Deep Learning10.1007/s40192-021-00227-2 ↗
19. Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites10.1002/pc.28614 ↗
20. A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis10.1098/rsif.2017.0844 ↗
21. Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data
22. Expanding Design Spaces in Digital Composite Materials: A Multi-Input Deep Learning Approach Enhanced by Transfer Learning and Multi-kernel Network10.1002/adts.202300465 ↗
23. Material-Response-Informed DeepONet and Its Application to Polycrystal Stress–Strain Prediction in Crystal Plasticity10.1007/s11837-024-06681-5 ↗
24. Uncertainty quantification of compressive after impact strength in laminated composites using improved Bayesian ResNet deep learning model10.1002/pc.29998 ↗
25. A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths10.1016/j.cma.2020.113234 ↗
26. Predicting stress, strain and deformation fields in materials and structures with graph neural networks10.1038/s41598-022-26424-3 ↗
27. O. Ronneberger, P. Fischer, T. Brox, 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-assisted Intervention, PT III, 2015, pp. 234-241.10.1007/978-3-319-24574-4_28 ↗
28. Performance evaluation of deep learning approaches for predicting mechanical fields in composites10.1007/s00366-024-01966-4 ↗
29. Layer-by-layer reconstruction of fatigue damages in composites from thermal images by a Residual U-Net10.1016/j.compscitech.2024.110712 ↗
30. A deep learning based framework for predicting temperature-dependent mesomechanical fields in particulate composites10.1016/j.compstruct.2025.119930 ↗
31. Automatic segmentation of microporous defects in composite film materials based on the improved attention U-Net module
32. Exploring the design space of discontinuous metal matrix composites through domain-knowledge enhanced machine learning10.1016/j.eml.2024.102176 ↗
33. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
34. CBAM: convolutional block attention module
35. Integrated convolutional and graph neural networks for predicting mechanical fields in composite microstructures
36. Voronoi diagrams-a survey of a fundamental geometric data structure10.1145/116873.116880 ↗
37. S. Gottschalk, M. Lin, D. Manocha, OBBTree: a hierarchical structure for rapid interference detection, International Conference on Computer Graphics and Interactive Techniques, ACM, pp. 171-180.10.1145/237170.237244 ↗
38. Ministry of Transport of the People’s Republic of China, Technical specification for construction of highway asphalt pavement, Beijing: China Communication Press.
39. Determining Representative Volume Elements of Asphalt Concrete Mixtures without Damage10.3141/2127-07 ↗
40. Computational micromechanical analysis of the representative volume element of bituminous composite materials10.1007/s11043-016-9296-x ↗
41. Tschoegl · 1989
42. A unified procedure for rapidly determining asphalt concrete discrete relaxation and retardation spectra10.1016/j.conbuildmat.2015.04.055 ↗
43. Three-dimensional mesomechanical complex modulus prediction for asphalt mortar considering conjunctive shell mechanism of interface transition zones and properties of coarse mastic
44. Characterizing asphalt mixtures with random aggregate gradations based on the three-dimensional locally homogeneous model10.1111/mice.12796 ↗