AI Lunch Series - Miles Cranmer
- AI Lunch Series - Miles Cranmerhttps://bluejeans.com/500985640Remote2021-05-05EDT12:00:00 ~ 2021-05-05EDT13:00:0015270
“Interpretable Machine Learning for Physics, using Symbolic Regression and Graph Neural Networks” Miles Cranmer (Princeton)
Abstract: “In this talk I will argue two points 1) Symbolic regression, a machine learning technique that fits data by iteratively searching the space of all possible analytic equations, should be a standard machine learning algorithm in physics. 2) Symbolic regression can be extended to high-dimensional spaces, such as to models for N-body simulations, using the method we have developed. To begin the talk, I will introduce symbolic regression (SR), which is a relatively old but underdeveloped technique. I will demonstrate our new high-performance open-source SR software PySR. I will then discuss our research contribution: a technique for extending SR to high-dimensional spaces by the use of a neural network. I will focus on Graph Neural Networks (GNNs), which are a physically motivated neural network architecture, and very relevant to many physical and astrophysical problems. As a validation, I will demonstrate that we can use this technique of a GNN to SR to extract the correct force laws and Hamiltonians from simple particle simulations. I will then show work applying our method to the Quijote dark matter simulations – where it finds a simple analytic formula to estimate the over-density of dark matter in a halo using learned metrics which summarize the nearby halos, giving a functional form that is more accurate than hand-derived formulas. Our approach simultaneously offers an interpretable ML technique for high-dimensional astrophysical data, and also a way of interpreting neural networks. (Demo code is available here)"
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