Scientific Data Mining: Distilling Free-Form Natural Laws from Experimental Data
published: Aug. 29, 2011, recorded: July 2011, views: 403
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We will show that by seeking dynamical invariants, we can go from finding just predictive models to finding deeper conservation laws. Applications to modeling physical and biological systems will be shown, and both deterministic and stochastic models will be considered.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !