Efficient Neural PDE-Solvers using Quantization Aware Training

Understand

In the past years, the application of neural networks as an alternative to classical numerical methods to solve Partial Differential Equations has emerged as a potential paradigm shift in this century-old mathematical field.

• However, in terms of practical applicability, computational cost remains a substantial bottleneck.
• Classical approaches try to mitigate this challenge by limiting the spatial resolution on which the PDEs are defined.
• For neural PDE solvers, we can do better: Here, we investigate the potential of state-of-the-art quantization methods on reducing computational costs.