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Active Learning for Directed Exploration of Complex SystemsPhysics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.
Document ID
20150008341
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Burl, Michael C.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Wang, Esther
(California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 18, 2015
Publication Date
June 14, 2009
Subject Category
Systems Analysis And Operations Research
Meeting Information
Meeting: International Conference on Machine Learning
Location: Montreal
Country: Canada
Start Date: June 14, 2009
End Date: June 18, 2009
Sponsors: International Machine Learning Society (IMLS)
Distribution Limits
Public
Copyright
Other
Keywords
oracles
simulations
kernel methods
information theory

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