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Activity Number:
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349
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #300805 |
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Title:
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Data Mining Dynamical Systems: Automated Symbolic System Identification for Exploratory Analysis
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Author(s):
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Michael D. Schmidt*+ and Hod Lipson
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Companies:
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Cornell University and Cornell University
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Address:
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32 Fairview Sq, Ithaca, NY, 14850,
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Keywords:
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Dynamical Systems ; Symbolic Regression ; Genetic Programming
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Abstract:
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Many branches of science and engineering represent dynamical systems mathematically, as sets of differential equations, derived laboriously from basic principles and experimentation. We are developing new approaches to reverse-engineer the analytical differential equations of a dynamical system automatically. We use genetic programming techniques to perturb and destabilize the system to reveal its hidden characteristics and to infer nonlinear symbolic relationships between variables. Our research has shown the ability to infer a seven-variable cell glycolysis system directly from data - the largest system inferred automatically to date. Our focus is to advance this promising approach to operate in high-noise and limited observability environments where manual methods for modeling are most overwhelmed.
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