Reference health
Prediction and optimization of mechanical properties of composites using convolutional neural networks
32/32 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
19 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 32 checked references that resolve
resolves10.1088/2399-1984/aa6aed
Nacre-inspired design of graphene oxide–polydopamine nanocomposites for enhanced mechanical properties and multi-functionalities
resolves10.1002/adfm.201300340
Biological and Bioinspired Composites with Spatially Tunable Heterogeneous Architectures
resolves10.1016/j.compositesb.2016.11.034
3D printing of polymer matrix composites: A review and prospective
resolves10.3389/fphy.2017.00039
The Ultrastructure of Bone and Its Relevance to Mechanical Properties
resolves10.1002/adem.201700549
Nature‐Inspired Lightweight Cellular Co‐Continuous Composites with Architected Periodic Gyroidal Structures
resolves10.1002/adma.201803334
3D Plate‐Lattices: An Emerging Class of Low‐Density Metamaterial Exhibiting Optimal Isotropic Stiffness
resolves10.1016/j.commatsci.2009.04.004
Simulations of delamination in CFRP laminates: Effect of microstructural randomness
resolves10.1016/j.commatsci.2013.05.006
Scaling function in conductivity of planar random checkerboards
resolves10.1109/TPAMI.2006.184
Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization
resolves10.1063/1.4946894
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
resolves10.1016/S0263-8223(02)00053-3
Post-buckling optimisation of composite stiffened panels using neural networks
resolves10.1039/C8MH00653A
Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
resolves10.1016/j.compositesb.2018.09.087
Material optimization of functionally graded plates using deep neural network and modified symbiotic organisms search for eigenvalue problems
resolves10.1111/mice.12422
Deep neural network with high-order neuron for the prediction of foamed concrete strength
resolves10.1016/j.neunet.2018.12.005
Response prediction of nonlinear hysteretic systems by deep neural networks
resolves10.1016/j.conbuildmat.2017.09.110
Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
resolves10.1016/S0045-7949(99)00185-6
Comparison of the numerical efficiency of different sequential linear programming based algorithms for structural optimisation problems
resolves10.1016/j.ijsolstr.2005.03.030
Benchmark case studies in structural design optimization using the force method
resolves10.1016/j.cma.2013.10.022
Failure mitigation in optimal topology design using a coupled nonlinear continuum damage model
resolves10.1016/j.cma.2014.11.012
Topology optimization of viscoelastic structures using a time-dependent adjoint method
resolves10.1109/TSMCA.2009.2012436
Optimal Contraction Theorem for Exploration–Exploitation Tradeoff in Search and Optimization
The 19 references without a DOI — listed, not checked
no DOI — not checked10.1016/j.compstruct.2019.111264_b0005
no DOI — not checked10.1016/j.compstruct.2019.111264_b0045
no DOI — not checkedCeramic matrix composites
no DOI — not checked10.1016/j.compstruct.2019.111264_b0070
no DOI — not checkedTopology optimization
no DOI — not checked10.1016/j.compstruct.2019.111264_b0095
no DOI — not checked10.1016/j.compstruct.2019.111264_b0100
no DOI — not checkedTaylor RL Feap-a finite element analysis program (2014).
no DOI — not checkedDropout: a simple way to prevent neural networks from overfitting
no DOI — not checked10.1016/j.compstruct.2019.111264_b0175
no DOI — not checked10.1016/j.compstruct.2019.111264_b0180
no DOI — not checked10.1016/j.compstruct.2019.111264_b0185
no DOI — not checkedAdaptation in natural and artificial systems
no DOI — not checkedRemoving the genetics from the standard genetic algorithm
no DOI — not checked10.1016/j.compstruct.2019.111264_b0200
no DOI — not checked10.1016/j.compstruct.2019.111264_b0205
no DOI — not checkedZhang C, Bengio S, Hardt M, Recht B, Vinyals O, Understanding deep learning requires rethinking generalization, arXiv preprint arXiv:1611.03530.
no DOI — not checkedGenetic algorithms in search, optimization, and machine learning
no DOI — not checked10.1016/j.compstruct.2019.111264_b0255
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