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

### Research (full list)

## Rediscovering orbital mechanics with machine learning

Could we discover the law of gravitation without even knowing the masses of planets in the solar system? In this paper we show how.

## Unsupervised Resource Allocation with GNNs

We show how to optimize resource allocation without knowing the true utility. We use this to learn a mock telescope observational survey from scratch.

## Bayesian neural networks for planetary instability

We describe a Bayesian neural network architecture that can accurately learn to predict dynamical (chaotic) instability in compact planetary systems. The network demonstrates surprisingly robust generalization to 5-planet systems.

## 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.

## 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.

## 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.

### Software (full list)

## High-Performance Symbolic Regression in Python

A library for interpretable machine learning, using symbolic regression, in Python

### Posts

## An introduction to likelihood-free inference

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

## The use and abuse of machine learning in astronomy

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

## Symmetries in Neural Networks

Summary of a Twitter discussion about symmetries in Neural Networks

### Related articles

## Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

By Charlie Wood. How can machine learning help us discover new theories of physics, rather than simply fitting data?

## 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?

## 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