Poster
in
Workshop: The Symbiosis of Deep Learning and Differential Equations II
On the impact of larger batch size in the training of Physics Informed Neural Networks
Shyam Sankaran · Hanwen Wang · Leonardo Ferreira Guilhoto · Paris Perdikaris
Abstract:
Physics Informed Neural Networks (PINNs) have demonstrated remarkable success in learning complex physical processes such as shocks and turbulence, but their applicability has been limited due to long training times. In this work, we explore the potential of large batch size training to save training time and improve final accuracy in PINNs. We show that conclusions about generalization gap brought by large batch size training on image classification tasks may not be compatible with PINNs. We conclude that larger batch sizes always beneficial to training PINNs.
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