  Physics-Informed Neural Networks (PINNs) have been shown to be an effective
way of incorporating physics-based domain knowledge into neural network models
for many important real-world systems. They have been particularly effective as
a means of inferring system information based on data, even in cases where data
is scarce. Most of the current work however assumes the availability of
high-quality data. In this work, we further conduct a preliminary investigation
of the robustness of physics-informed neural networks to the magnitude of noise
in the data. Interestingly, our experiments reveal that the inclusion of
physics in the neural network is sufficient to negate the impact of noise in
data originating from hypothetical low quality sensors with high
signal-to-noise ratios of up to 1. The resultant predictions for this test case
are seen to still match the predictive value obtained for equivalent data
obtained from high-quality sensors with potentially 10x less noise. This
further implies the utility of physics-informed neural network modeling for
making sense of data from sensor networks in the future, especially with the
advent of Industry 4.0 and the increasing trend towards ubiquitous deployment
of low-cost sensors which are typically noisier.
