Learning Symbolic Physics with Graph Networks

no code implementations • 12 Sep 2019 • Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho

We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization.

Inductive Bias Symbolic Regression +1

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates

2 code implementations • 21 Aug 2019 • Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho

We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution.

Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy

1 code implementation • 2 Aug 2017 • Miles D. Cranmer, Benjamin R. Barsdell, Danny C. Price, Jayce Dowell, Hugh Garsden, Veronica Dike, Tarraneh Eftekhari, Alexander M. Hegedus, Joseph Malins, Kenneth S. Obenberger, Frank Schinzel, Kevin Stovall, Gregory B. Taylor, Lincoln J. Greenhill

Computing blocks in the library are designed for applications such as interferometry, pulsar dedispersion and timing, and transient search pipelines.

Instrumentation and Methods for Astrophysics Distributed, Parallel, and Cluster Computing Instrumentation and Detectors