Reading Recommendation: On the generalization of PINNs outside the training domain and the hyperparameters influencing it

The article examines the generalization of Physics-informed neural networks (PINNs) beyond training domains, emphasizing predictive accuracy and adherence to physical principles. It challenges the role of overparametrization, suggesting it may promote overfitting rather than enhancing generalization in scientific machine learning.

Interesting: Lecture Series in AI: “How Could Machines Reach Human-Level Intelligence?” by Yann LeCun

Yann LeCun’s lecture discusses the limitations of current AI, emphasizing the need for a cognitive architecture with a predictive world model. This model uses a Joint Embedding Predictive Architecture (JEPA) to enable planning and understanding, aiming for human-level intelligence.

New arXiv Preprint: Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution

The article explores the integration of Explainable Artificial Intelligence (XAI) in enhancing deep learning performance. Focusing on regression problems, it introduces a feature selection pipeline using Integrated Gradients and k-means clustering, applied to blade vibration analysis in turbo machinery development.