ReLU Activation's Limitations in Physics-Informed Machine Learning

This ArXiv paper highlights a crucial constraint in the application of ReLU activation functions within physics-informed machine learning models. The findings likely necessitate a reevaluation of architecture choices for specific tasks and applications, driving innovation in model design.

• •ReLU activation's performance is being questioned in the context of physics-informed models.
• •The research likely identifies specific scenarios where ReLU underperforms.
• •The study could lead to the adoption of alternative activation functions in the field.