  Scientific machine learning (SciML) has emerged as a versatile approach to
address complex computational science and engineering problems. Within this
field, physics-informed neural networks (PINNs) and deep operator networks
(DeepONets) stand out as the leading techniques for solving partial
differential equations by incorporating both physical equations and
experimental data. However, training PINNs and DeepONets requires significant
computational resources, including long computational times and large amounts
of memory. In search of computational efficiency, training neural networks
using half precision (float16) rather than the conventional single (float32) or
double (float64) precision has gained substantial interest, given the inherent
benefits of reduced computational time and memory consumed. However, we find
that float16 cannot be applied to SciML methods, because of gradient divergence
at the start of training, weight updates going to zero, and the inability to
converge to a local minima. To overcome these limitations, we explore mixed
precision, which is an approach that combines the float16 and float32 numerical
formats to reduce memory usage and increase computational speed. Our
experiments showcase that mixed precision training not only substantially
decreases training times and memory demands but also maintains model accuracy.
We also reinforce our empirical observations with a theoretical analysis. The
research has broad implications for SciML in various computational
applications.
