Explainable AI Framework Validates Neural Networks for Physics Modeling
Analysis
This research explores the use of explainable AI to validate neural networks as surrogates for physics-based models, focusing on constitutive relations. The paper's contribution lies in providing a framework to assess the reliability and interpretability of these AI-driven surrogates.
Key Takeaways
- •Presents a framework for validating neural networks in physics applications.
- •Emphasizes explainable AI for understanding and interpreting model behavior.
- •Addresses the use of AI surrogates for complex constitutive modeling.
Reference
“The research focuses on learning constitutive relations using neural networks.”