Forecasting Damage Mechanics by Deep Learning

Duyen Le Hien Nguyen, Dieu Thi Thanh Do, Jaehong Lee, Timon Rabczuk, Hung Nguyen-Xuan
2019 Computers Materials & Continua  
We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of
more » ... hape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale. The predicted results by deep learning algorithms are well-agreed with experimental data.
doi:10.32604/cmc.2019.08001 fatcat:x43kidqjp5bhrfngyrujwuwkxe