  Physics-based simulations are often used to model and understand complex
physical systems and processes in domains like fluid dynamics. Such
simulations, although used frequently, have many limitations which could arise
either due to the inability to accurately model a physical process owing to
incomplete knowledge about certain facets of the process or due to the
underlying process being too complex to accurately encode into a simulation
model. In such situations, it is often useful to rely on machine learning
methods to fill in the gap by learning a model of the complex physical process
directly from simulation data. However, as data generation through simulations
is costly, we need to develop models, being cognizant of data paucity issues.
In such scenarios it is often helpful if the rich physical knowledge of the
application domain is incorporated in the architectural design of machine
learning models. Further, we can also use information from physics-based
simulations to guide the learning process using aggregate supervision to
favorably constrain the learning process. In this paper, we propose PhyDNN, a
deep learning model using physics-guided structural priors and physics-guided
aggregate supervision for modeling the drag forces acting on each particle in a
Computational Fluid Dynamics-Discrete Element Method(CFD-DEM). We conduct
extensive experiments in the context of drag force prediction and showcase the
usefulness of including physics knowledge in our deep learning formulation both
in the design and through learning process. Our proposed PhyDNN model has been
compared to several state-of-the-art models and achieves a significant
performance improvement of 8.46% on average across all baseline models. The
source code has been made available and the dataset used is detailed in [1, 2].

Machine Learning;Computational Physics;Machine Learning