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Decision Manifold Approximation for Physics-Based SimulationsWith the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.
Document ID
20160003613
Acquisition Source
Langley Research Center
Document Type
Technical Memorandum (TM)
Authors
Wong, Jay Ming (Massachusetts Univ. Amherst, MA, United States)
Samareh, Jamshid A. (NASA Langley Research Center Hampton, VA, United States)
Date Acquired
March 22, 2016
Publication Date
January 1, 2016
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
NASA/TM-2016-219004
L-20657
NF1676L-23423
Funding Number(s)
WBS: WBS 388496.04.01.02
Distribution Limits
Public
Copyright
Public Use Permitted.

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