Pages that link to "Item:Q2237439"
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The following pages link to A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites (Q2237439):
Displaying 11 items.
- A wavelet-based learning approach assisted multiscale analysis for estimating the effective thermal conductivities of particulate composites (Q2021276) (← links)
- Application of general regression neural network in identifying interfacial parameters under mixed-mode fracture (Q2083815) (← links)
- Variational Bayesian approximation of inverse problems using sparse precision matrices (Q2138759) (← links)
- Surrogate modeling of the effective elastic properties of spherical particle-reinforced composite materials (Q2160353) (← links)
- Bayesian inference of non-linear multiscale model parameters accelerated by a deep neural network (Q2175257) (← links)
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks (Q2190108) (← links)
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites (Q2319390) (← links)
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems (Q2679283) (← links)
- Machine learning meta-models for fast parameter identification of the lattice discrete particle model (Q6164296) (← links)
- Probabilistic physics-guided transfer learning for material property prediction in extrusion deposition additive manufacturing (Q6185217) (← links)
- CNN-based prediction of microstructure-derived random property fields of composite materials (Q6595874) (← links)
