BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Symbolic Regression for Model Discovery in Python and Julia - Mile
 s Cranmer - DAMTP\, University of Cambridge
DTSTART:20250605T120000Z
DTEND:20250605T130000Z
UID:TALK227173@talks.cam.ac.uk
CONTACT:Jack Atkinson
DESCRIPTION:Symbolic regression libraries present a framework for automati
 cally discovering mathematical models directly from data\, bridging the ga
 p between data-driven methods and analytical science. PySR is an open-sour
 ce library that provides a high-performance framework for symbolic regress
 ion\, pairing a flexible multi-population evolutionary algorithm with its 
 lightning-fast backend\, SymbolicRegression.jl. This architecture allows u
 sers to seamlessly integrate PySR with existing Python or Julia workflows
 —whether in a laptop setup or distributed across a cluster. In this talk
 \, I will introduce PySR’s modular ecosystem of symbolic regression modu
 les\, and show how it can be “plugged into” existing Julia libraries t
 o automatically learn closed-form equations tailored to the user’s domai
 n. I will also highlight PySR’s new template expressions feature\, which
  enables learning within a specific functional form\, thus allowing scient
 ists a flexible way of embedding domain knowledge in the search. I will al
 so discuss PySR’s interface with deep learning as an interpretability to
 ol.\n\nTo join remotely use the following link:\nhttps://cam-ac-uk.zoom.us
 /j/82860255019?pwd=ONrFS2La00nTh8iVQQdLl2JYzGattP.1\n> Passcode: 071461
LOCATION:JJ Thomson Seminar Room\, Maxwell Centre\, and on Zoom
END:VEVENT
END:VCALENDAR