PINNfluence: Interpreting PINNs through Influence Functions
📰 ArXiv cs.AI
Learn to interpret Physics-informed Neural Networks (PINNs) using influence functions with PINNfluence, a novel framework for understanding PINN behavior
Action Steps
- Apply influence functions to PINNs using PINNfluence to identify key training data points
- Analyze the influence of each training data point on the PINN's predictions
- Configure PINNfluence to attribute training data to specific outputs or predictions
- Test the robustness of PINNfluence on various PINN architectures and datasets
- Compare the interpretability of PINNs using PINNfluence with other attribution methods
Who Needs to Know This
Data scientists and researchers working with PINNs can benefit from this framework to improve model interpretability and transparency, while engineers can use it to better understand and debug their PINN-based systems
Key Insight
💡 Influence functions can be used to attribute training data to specific predictions in PINNs, providing a new level of model interpretability
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🚀 Introducing PINNfluence: a novel framework for interpreting Physics-informed Neural Networks (PINNs) using influence functions 🤖
Key Takeaways
Learn to interpret Physics-informed Neural Networks (PINNs) using influence functions with PINNfluence, a novel framework for understanding PINN behavior
Full Article
Title: PINNfluence: Interpreting PINNs through Influence Functions
Abstract:
arXiv:2409.08958v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their behavior remains largely opaque and is typically understood through failure mode analyses rather than explicit interpretability. To address this issue, we introduce PINNfluence, a training data attribution framework for interpreting PINNs based on influence functio
Abstract:
arXiv:2409.08958v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their behavior remains largely opaque and is typically understood through failure mode analyses rather than explicit interpretability. To address this issue, we introduce PINNfluence, a training data attribution framework for interpreting PINNs based on influence functio
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