Predictive modelling

Quantum mechanics

Algorithm

Composite number

Ecology

Biology

Power (physics)

Computer science

Micromechanics

Homogenization (climate)

Physics

Reliability (semiconductor)

Artificial intelligence

Artificial neural network

Biodiversity

Machine learning

International Journal for Numerical Methods in Engineering

In this article, an investigation was carried out to verify hybrid models capabilities to predict the effective properties of heterogeneous materials. A hybrid model is developed by combining artificial neural networks and micromechanical modeling. The homogenization approach used in this study is mainly based on Eshelby's inclusion problem. The model, once trained on an Eshelby's tensors database, showed an excellent predictive capabilities of the effective mechanical behavior and local stresses in heterogeneous materials. The obtained results with are compared to numerical estimations which are often costly in terms of computational time. The results presented in this work show that the developed hybrid model can provide a significant computational time saving by a factor up to 2000 for phases while maintaining its accuracy and reliability.

Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models