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A deep learning energy method for hyperelasticity and viscoelasticity
The potential energy formulation and deep learning are merged to solve partial differential equations governing the deformation in hyperelastic and viscoelastic materials.
Meshless physics-informed deep learning method for three-dimensional solid mechanics
We show that the DCM can capture the response qualitatively and quantitatively, without the need for any data generation using other numerical methods such as the FEM.
Topology optimization of 2D structures with nonlinearities using deep learning
We have considered the case of materials with a linear elastic response with and without stress constraint.
Prediction and optimization of mechanical properties of composites using convolutional neural networks
Once the training process is completed, the developed model is validated using data unseen during training.
