On 27 November 2020, Prof. Yang-Hui He explored the intersection of cosmology, string theory, and computational methods in his talk "The Universe as Big Data."
Physics Meets Data Science
"Treating physics problems as data-mining exercises can reveal unexpected patterns and guide intuition in domains far removed from traditional AI applications."
The lecture examined how machine learning and data science techniques are transforming theoretical physics—from analyzing the enormous "landscape" of possible string theory solutions to searching for hidden mathematical structures in fundamental equations.
The String Theory Landscape
String theory predicts not one unique universe but a vast "landscape" of roughly 10⁵⁰⁰ possible vacuum states—each representing a different way our universe's laws of physics could be configured.
This staggering number makes traditional analytical methods impractical. Machine learning offers a new approach:
- Pattern recognition: Identifying which vacuum states have desired properties
- Classification: Grouping similar solutions to understand the landscape's structure
- Prediction: Guessing properties of unexplored regions
- Optimization: Finding solutions that match observations of our universe
Hidden Structures in Equations
Prof. He demonstrated how neural networks can discover mathematical relationships that humans might miss—for example, finding unexpected symmetries in the equations governing particle physics or identifying correlations between seemingly unrelated mathematical objects.
Applications Beyond String Theory
The techniques apply to:
- Algebraic geometry: Understanding the shapes that underlie fundamental physics
- Number theory: Finding patterns in prime numbers and modular forms
- Cosmology: Analyzing cosmic microwave background data for signatures of early universe physics
- Quantum field theory: Simplifying complex calculations through learned patterns
A New Way to Think
This approach represents a philosophical shift: treating the universe's mathematical structures as datasets to be explored with computational tools, complementing traditional pen-and-paper theoretical work with algorithmic discovery.
Watch the full lecture:
youtube.com/watch?v=feGuGLTUziI