Title: Recent Advancement of Scientific Machine Learning

Abstract: Machine learning has revolutionized scientific computing, offering unprecedented computational efficiency, flexibility, and applicability to real-world challenges. However, traditional machine learning approaches often overlook the rich insights provided by existing physical laws or mathematical properties. This talk explores the latest advancements in AI techniques that respect existing physical laws or mathematical properties, enabling the resolution of complex problems in physics and engineering. We will showcase the effectiveness of these methods through diverse applications, highlighting their potential to transform the landscape of scientific and engineering computations.