Seminar: Numerial Methods for Partial Differential Equations

Speaker;  Miles Cranmer (Princeton)

Title:  Interpretable Machine Learning for physics

Abstract:

Would Kepler have discovered his laws if machine learning had been around in 1609? Or would he have been satisfied with the accuracy of some black box regression model, leaving Newton without the inspiration to find the law of gravitation? In this talk I will present a review of some industry-oriented machine learning algorithms, and discuss a major issue facing their use in the natural sciences: a lack of interpretability. I will then outline several approaches I have created with collaborators to help address these problems, based largely on a mix of structured deep learning and symbolic methods. This will include an introduction to the PySR/SymbolicRegression.jl software (https://astroautomata.com/PySR), a Python/Julia package for high-performance symbolic regression. I will conclude by demonstrating applications of such techniques and how we may gain new insights from such results.

         ZOOM Link: 

                    https://mit.zoom.us/j/99160816852