1. Ao, L., Jin, Y., Pang, H., (2021). Prediction of ROP based on Artificial Neural Network with Long and Short Memory (LSTM), in: 55th U.S. Rock Mechanics / Geomechanics Symposium 2021. pp. 207–213.
2. Gradient amplification: An efficient way to train deep neural networks;Basodi;Big Data Min Anal,2020
3. Machine learning methods applied to drilling rate of penetration prediction and optimization - A review;Barbosa;J. Pet. Sci. Eng.,2019
4. Bartosik, S.C. and Amirlatih, A., (2020). Machine Learning Assisted Geosteering, 54th Rock Mechanics/Geomechanics Symposium held in Golden, Colorado, USA, 28-June to 1-July-2020. ARMA 20-1662.
5. Chen, X., Yang, J., Gao, D., (2018). Drilling Performance Optimization Based on Mechanical Specific Energy Technologies, in: Drilling. https://doi.org/10.5772/intechopen.75827