ThoughtGallery.org > The First AI Simulation of the Universe Is Fast and Accurate—And We Don’t Know Why

The First AI Simulation of the Universe Is Fast and Accurate—And We Don’t Know Why

A full understanding of the evolution of the universe’s structure is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the universe and compare the findings to computer simulations. Simulating the movement of billions of particles over billions of years is a daunting task, however, even when using the simplest physical models.

In this lecture, Shirley Ho will discuss her team’s work building a deep neural network that learns from a set of pre-run numerical simulations and predicts the large scale structure of the universe. Extensive analysis demonstrates that their deep-learning technique outperforms the commonly used fast approximate simulation method in predicting cosmic structure in the non-linear regime. They also show that their method can accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. This ability to extrapolate outside its training set is highly unexpected and remains a mystery.

Shirley Ho’s research interests have ranged from fundamental cosmological measurements to exoplanet statistics to using machine learning to estimate how much dark matter is in the universe. Ho has broad expertise in theory, observation and data science. Her recent interest has been on understanding and developing novel tools in statistics and machine learning techniques and applying them to astrophysical challenges. Her goal is to understand the universe’s beginning, evolution and ultimate fate.

Ho earned her Ph.D. in astrophysical sciences from Princeton University in 2008 and her bachelor’s degrees in computer science and physics from the University of California, Berkeley, in 2004. She was a Chamberlain fellow and a Seaborg fellow at Lawrence Berkeley National Laboratory before joining Carnegie Mellon University in 2011 as an assistant professor. She became the Cooper Siegel Career Development Chair Professor and was appointed associate professor with tenure in 2016. She moved to Lawrence Berkeley National Laboratory as a senior scientist in 2016. Ho joined the Simons Foundation in 2018 as leader of the Cosmology X Data Science group at the Center for Computational Astrophysics.

TEA: 4:15-5:00pm
LECTURE: 5:00-6:15pm










When: Wed., Feb. 26, 2020 at 5:00 pm - 6:15 pm
Where: Simons Foundation
160 Fifth Ave., 2nd Floor
646-654-0066
Price: Free
Buy tickets/get more info now
See other events in these categories:

A full understanding of the evolution of the universe’s structure is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the universe and compare the findings to computer simulations. Simulating the movement of billions of particles over billions of years is a daunting task, however, even when using the simplest physical models.

In this lecture, Shirley Ho will discuss her team’s work building a deep neural network that learns from a set of pre-run numerical simulations and predicts the large scale structure of the universe. Extensive analysis demonstrates that their deep-learning technique outperforms the commonly used fast approximate simulation method in predicting cosmic structure in the non-linear regime. They also show that their method can accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. This ability to extrapolate outside its training set is highly unexpected and remains a mystery.

Shirley Ho’s research interests have ranged from fundamental cosmological measurements to exoplanet statistics to using machine learning to estimate how much dark matter is in the universe. Ho has broad expertise in theory, observation and data science. Her recent interest has been on understanding and developing novel tools in statistics and machine learning techniques and applying them to astrophysical challenges. Her goal is to understand the universe’s beginning, evolution and ultimate fate.

Ho earned her Ph.D. in astrophysical sciences from Princeton University in 2008 and her bachelor’s degrees in computer science and physics from the University of California, Berkeley, in 2004. She was a Chamberlain fellow and a Seaborg fellow at Lawrence Berkeley National Laboratory before joining Carnegie Mellon University in 2011 as an assistant professor. She became the Cooper Siegel Career Development Chair Professor and was appointed associate professor with tenure in 2016. She moved to Lawrence Berkeley National Laboratory as a senior scientist in 2016. Ho joined the Simons Foundation in 2018 as leader of the Cosmology X Data Science group at the Center for Computational Astrophysics.

TEA: 4:15-5:00pm
LECTURE: 5:00-6:15pm
Buy tickets/get more info now