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Title: The Robotic Scientist: Distilling Natural Laws from Experimental Data, from Cognitive Robotics to Computational Biology

Can machines discover analytical laws automatically? For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalencmore »e 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. By seeking dynamical invariants and symmetries, we show how we can go from finding just predictive models to finding deeper conservation laws. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the “alphabet” used to describe those systems. Application to modeling physical and biological systems will be shown.« less
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Title: The Robotic Scientist: Distilling Natural Laws from Experimental Data, from Cognitive Robotics to Computational Biology
Authors:
Publication Date: 2011-10-25
OSTI Identifier: 1043246
Resource Type: Multimedia
Resource Relation: Conference: Wednesday Colloquium Series, Princeton Plasma Physics Laboratory, Princeton, New Jersey, presented on October 25, 2011
Research Org: PPPL (Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States))
Sponsoring Org: USDOE Office of Science (SC)
Subject: Robot Technology
Country of Publication: United States
Language: English
Run Time:
System Entry Date: 2016-06-27