DDPS - A Flexible and Generalizable XAI Framework for Scientific Deep Learning
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a flexible and generalizable explainable AI (XAI) framework for scientific deep learning in this 53-minute talk. Delve into the challenges of interpretability and generalization in modeling physical systems, and discover an innovative approach inspired by human thinking processes. Learn how this framework probes trained neural networks, fits interpretable models using integral equations, and improves out-of-distribution (OOD) generalization. Examine applications in solid mechanics, fluid mechanics, and transport, showcasing the framework's potential for providing analytical representations of deep neural networks and enhancing OOD performance. Gain insights into theory-guided machine learning, functional data analysis, and the synergistic integration of interpretable models with black-box deep learning techniques.