Speeding up and reducing memory usage for scientific machine learning via mixed precision

Understand

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.