Scientific Machine Learning - Where Physics-based Modeling Meets Data-driven Learning
Explore the emerging field of scientific machine learning in this lecture from the Santa Fe Institute. Delve into the synergy between physics-based modeling and data-driven learning, examining their application in complex scientific, engineering, and medical problems. Discover how this approach combines the predictive power and interpretability of physics-based models with the flexibility and computational scalability of machine learning. Learn about specific methods that integrate reduced-order modeling with machine learning, illustrated through examples such as modeling combustion in rocket engines and tubular reactors. Gain insights into the challenges and opportunities of harnessing data for knowledge extraction and decision-making in scientific contexts, and understand the importance of combining domain knowledge with modern data-driven techniques.