Knowledge-Guided Transfer Learning for Modeling Subsurface Phenomena Under Data Paucity

Knowledge transfer from machine learning (ML) models, pre-trained on large corpuses has been leveraged effectively in domains like natural language processing and computer vision to improve model generalization. The knowledge transfer prowess of ML and especially deep learning (DL) models has been demonstrated to be especially effective under data paucity of the target task. Many scientific phenomena require the execution of costly simulations to estimate the process of interest. Predicting molecular configuration of fluids confined in porous media is one such problem of extreme relevance in many scientific applications, the study of which requires the execution of expensive Molecular Dynamics (MD) simulations. However, due to the cost of MD, large scale simulations become intractable. Hence, in this work, we develop a novel science-guided deep learning framework NanoNet-SG to emulate MD simulations. Our proposed NanoNet-SG model leverages scientific domain knowledge in conjunction with knowledge from pre-trained knowledge bases for estimating molecular configuration of fluid mixtures. Through rigorous experimentation, we demonstrate that our proposed NanoNet-SG model yields good generalization performance (minimum performance improvement of 16.26 % over baselines) and yields predictions that are consistent with known scientific domain rules despite being trained on a low volume of MD simulation data (i.e., data paucity).