  Modeling the precise dynamics of off-road vehicles is a complex yet essential
task due to the challenging terrain they encounter and the need for optimal
performance and safety. Recently, there has been a focus on integrating nominal
physics-based models alongside data-driven neural networks using Physics
Informed Neural Networks. These approaches often assume the availability of a
well-distributed dataset; however, this assumption may not hold due to regions
in the physical distribution that are hard to collect, such as high-speed
motions and rare terrains. Therefore, we introduce a physics-informed data
augmentation methodology called PIAug. We show an example use case of the same
by modeling high-speed and aggressive motion predictions, given a dataset with
only low-speed data. During the training phase, we leverage the nominal model
for generating target domain (medium and high velocity) data using the
available source data (low velocity). Subsequently, we employ a
physics-inspired loss function with this augmented dataset to incorporate prior
knowledge of physics into the neural network. Our methodology results in up to
67% less mean error in trajectory prediction in comparison to a standalone
nominal model, especially during aggressive maneuvers at speeds outside the
training domain. In real-life navigation experiments, our model succeeds in 4x
tighter waypoint tracking constraints than the Kinematic Bicycle Model (KBM) at
out-of-domain velocities.
