Scientific Machine Learning: Applications and Algorithms

This lecture introduces the concept of scientific machine learning, focusing on applications in various sciences and the development of new algorithms. The instructor discusses the challenges of applying machine learning to sparse data and explains how physics-inspired algorithms can improve spectral methods. Through examples and analogies, the lecture explores the intersection of machine learning and physics, highlighting the importance of understanding the underlying theory to advance scientific machine learning.