A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads
A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
Data-driven and physics-informed deep learning operators for solution of heat conduction equation with parametric heat source
Deep neural networks as universal approximators of partial differential equations (PDEs)
have attracted attention in numerous scientific and technical circles with the introduction of …
have attracted attention in numerous scientific and technical circles with the introduction of …
Deep learning framework for material design space exploration using active transfer learning and data augmentation
Neural network-based generative models have been actively investigated as an inverse
design method for finding novel materials in a vast design space. However, the applicability …
design method for finding novel materials in a vast design space. However, the applicability …
Deep learning-accelerated designs of tunable magneto-mechanical metamaterials
Metamaterials are artificially structured materials with unusual properties, such as negative
Poisson's ratio, acoustic band gap, and energy absorption. However, metamaterials made of …
Poisson's ratio, acoustic band gap, and energy absorption. However, metamaterials made of …
Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads
Abstract Deep Operator Network (DeepONet), a recently introduced deep learning operator
network, approximates linear and nonlinear solution operators by taking parametric …
network, approximates linear and nonlinear solution operators by taking parametric …
Automatic detection and classification of steel surface defect using deep convolutional neural networks
S Wang, X **a, L Ye, B Yang - Metals, 2021 - mdpi.com
Automatic detection of steel surface defects is very important for product quality control in the
steel industry. However, the traditional method cannot be well applied in the production line …
steel industry. However, the traditional method cannot be well applied in the production line …
Deep learning-based heterogeneous strategy for customizing responses of lattice structures
Designing lattice structures with tunable mechanical behavior for multi-functional
applications is of great significance. However, the inverse design of lattice structure for the …
applications is of great significance. However, the inverse design of lattice structure for the …
Modeling structure-property relationships with convolutional neural networks: Yield surface prediction based on microstructure images
The use of micromechanics in conjunction with homogenization theory allows for the
prediction of the effective mechanical properties of materials based on microstructural …
prediction of the effective mechanical properties of materials based on microstructural …