REAct: Rational Exponential Activation for Better Learning and Generalization in PINNs
Sourav Mishra, Shreya Hallikeri, Suresh Sundaram·March 04, 2025
Summary
REAct, a learnable activation function for Physics-Informed Neural Networks, excels in function approximation, noise rejection, and parameter estimation, outperforming standard activations by three orders of magnitude on heat problems. Its flexibility enhances PINN's optimization and generalization, making it suitable for diverse physical systems.
Overview of REAct
What is REAct?
Definition and purpose of REAct
Key Features of REAct
Function approximation capabilities
Noise rejection abilities
Parameter estimation improvements
Performance of REAct in Heat Problems
Comparative Analysis
Performance metrics against standard activations
Quantitative results on heat problems
Optimization and Generalization in PINNs
Enhancements in Optimization
How REAct improves the optimization process
Generalization Across Physical Systems
Application of REAct in various physical systems
Implementation of REAct in Physics-Informed Neural Networks
Data Collection for REAct
Methods for collecting data relevant to REAct
Data Preprocessing for REAct
Techniques for preparing data for use with REAct
Case Studies and Applications
Real-World Examples
Detailed case studies demonstrating REAct's effectiveness
Future Directions
Potential areas for further research and development
Conclusion
Summary of REAct's Contributions
Recap of REAct's impact on Physics-Informed Neural Networks
Implications for Future Research
Outlook on the future of REAct and its applications
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does the REAct activation function integrate with Physics-Informed Neural Networks to improve performance?
In what ways does the flexibility of REAct enhance the optimization and generalization of PINNs across different physical systems?
What are the key implementation details of the REAct activation function that contribute to its superior performance?