Department Of Computational And Applied Mechanics 1

1. Evolutionary automated radial basis function neural network for multiphase flowing bottom-hole pressure prediction
2. Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework
3. Machine learning-enhanced fully coupled fluid–solid interaction models for proppant dynamics in hydraulic fractures
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