Mathematics for Data-Driven Modeling - The Science of Crystal Balls

Explore the cutting-edge field of data-driven modeling in this Rothschild Lecture by Professor Yannis Kevrekidis from Princeton University. Discover how modern mathematical techniques are revolutionizing the process of making predictions directly from observational data, bypassing traditional equation-based modeling. Learn about the evolution of mathematical modeling from conventional methods to innovative algorithms that analyze models without closed-form equations. Gain insights into the underlying mathematics and "serious thinking" behind these seemingly magical "crystal ball" prediction methods. Examine real-world examples demonstrating this new approach to deriving predictions from data, and understand how it relates to traditional modeling techniques. Delve into the future of mathematical modeling and its implications for various scientific disciplines.