astro automata focuses on automating astrophysics research with AI. It is both a personal research blog and collection of related content.

Miles Cranmer

Miles Cranmer

I’m a PhD candidate at Princeton trying to accelerate astrophysics with AI.

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Research (full list)

Discovering Symbolic Models from Deep Learning

We describe a technique for converting a deep learning model into an analytic equation, focusing on graph networks. We validate it on injected force laws and Hamiltonians, and then discover a new equation to accurately predict the overdensity of dark matter from its environment.

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Lagrangian Neural Nets

We show how one can learn a Lagrangian from data with a Neural Network. Such an architecture conserves energy in a learned simulator without requiring canonical coordinates.

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Normalizing Flows for Stellar Isochrones

A normalizing flow is a highly flexible probability distribution, perfect for the nonlinear surface of an HR diagram. Here, we show how to learn a flow for Gaia’s HR diagram.

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Posts

An introduction to likelihood-free inference

I give an introduction to likelihood-free (simulation-based) inference for scientists.

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The use and abuse of machine learning in astronomy

I argue that machine learning is both underused and overused in astrophysics.

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Symmetries in Neural Networks

Summary of a Twitter discussion about symmetries in Neural Networks

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Related articles

How Self-Driving Telescopes Could Transform Astronomy

By Ryan F. Mandelbaum. What if an autonomously operating telescope, free from human biases and complications, could find the solutions we’ve been missing?

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AI Will Help Scientists Ask More Powerful Questions

By Pushmeet Kohli. Self-learning systems can discover hidden patterns in immense data sets, transcending what humans could ever find on their own

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