Accelerating Discovery: Distilling Natural Laws from Experimental Data, from physics to biology
Cosponsored with the Collective Dynamics of Complex Systems (CoCo) Research Group
February 11, 2013
Academic Building A G008, 5:00 PM
Can machines discover scientific laws automatically? Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. This talk will outline a series of recent research projects, starting with self-reflecting robotic systems, and ending with machines that can formulate hypotheses, design experiments, and interpret the results, to discover new scientific laws. We will see examples from psychology to cosmology, from classical physics to modern physics, from big science to small science.
Hod Lipson is an Associate Professor of Mechanical & Aerospace Engineering and Computing & Information Science at Cornell University in Ithaca, NY. He directs the Creative Machines Lab, which focuses on novel ways for automatic design, fabrication and adaptation of virtual and physical machines. He has led work in areas such as evolutionary robotics, multi-material functional rapid prototyping, machine self-replication and programmable self-assembly. Lipson received his Ph.D. from the Technion -Israel Institute of Technology in 1998, and continued to a postdoc at Brandeis University and MIT. His research focuses primarily on biologically-inspired approaches, as they bring new ideas to engineering and new engineering insights into biology. For more information visit http://www.mae.cornell.edu/lipson.
See Schmidt M., Lipson H. (2009) “Distilling Free-Form Natural Laws from Experimental Data,”
Science, Vol. 324, no. 5923, pp. 81 – 85. Try it on your own data
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