Learning fundamental properties of physical systems with machine learning

Using data-driven methods and statistical modeling to uncover, unguided by existing theory, the fundamental properties of observed physical systems, this team is using software and data to provide a new pathway to develop a theoretical understanding of the physical world. The data for this project spans a diverse set of disciplines including materials science and astrophysics. This research represents an early step in a potentially massive shift in how academic research teams can use collected data: not just to test or reject scientific models that already exist, or the models we’ve used to collect the data, but to use machine learning and inference to directly form and create new scientific models that define our scientific understanding. The field of data science is in the very early stages of that potential shift, and this project explores the possibility of that future. BIDS Senior Fellow Joshua Bloom leads the project, with BIDS Senior Fellow Fernando Pérez collaborating.

BIDS Affiliates

Joshua Bloom

Astronomy; Center for Time-Domain Informatics
Co-I for Moore/Sloan Data Science Environments

Fernando Pérez

Statistics
Co-I for Moore/Sloan Data Science Environments