Automated Knowledge Discovery from Simulators

Burl, Michael C.; DeCoste, Dennis; Enke, B. L.; Mazzoni, Dominic; Merline, W. J.; Scharenbroich, Lucas

doi:10.1137/1.9781611972764.8

Cited by 11 publications

(6 citation statements)

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“…Similarly, in an ongoing study, we are using an artificial intelligence tool, sim_learn (Burl et al, 2006), to verify the accuracy of our scaled estimates for the (283) Emma family parent body size. The best match found so far by the intelligent tool is equivalent to a Basalt_4.6_55.4_1.38 simulation with a parent body diameter of 167 km.…”

Section: Caveats In Our Modeling Resultsmentioning

confidence: 99%

Size–frequency distributions of fragments from SPH/N-body simulations of asteroid impacts: Comparison with observed asteroid families

Durda

Bottke

Nesvorný

et al. 2007

Icarus

189

243

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Section: Caveats In Our Modeling Resultsmentioning

confidence: 99%

Size–frequency distributions of fragments from SPH/N-body simulations of asteroid impacts: Comparison with observed asteroid families

Durda

Bottke

Nesvorný

et al. 2007

Icarus

189

243

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“…Better, Glover, and Laguna (2007) present an approach to simulation optimization where data mining methods are used during optimization in order to separate good quality from bad quality solutions in the parameter space. A similar approach is introduced by Burl et al (2006) where active learning with support vector machines is used to identify parameter configurations for future simulation runs which are expected to lead to a high information gain. Robinson (2005) presents a tool based on Microsoft Excel which can be coupled to the simulation software SIMUL8 for the analysis of simulation output data.…”

Section: Related Work -Support For Data Analysismentioning

confidence: 99%

Towards assisted input and output data analysis in manufacturing simulation: The EDASim approach

Bogon

Timm

Jessen

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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Discrete-event simulation has been established as an important methodology in various domains. In particular in the automotive industry, simulation is used to plan, control, and monitor processes including the flow of material and information. Procedure models help to perform simulation studies in a structured way and tools for data preparation or statistical analysis provide assistance in some phases of simulation studies. However, there is no comprehensive data assistance following all phases of such procedure models. In this article, a new approach combining assistance functionalities for input and output data analysis is presented. The developed tool -EDASim -focuses on supporting the user in selection, validation, and preparation of input data as well as to assist the analysis of output data. The proposed methods have been implemented and initial evaluations of the concepts have led to promising feedback from practitioners.

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“…A research area known as Design and Analysis of Computer Experiments (DACE) (Sacks et al, 1989) uses statistical methods, including kriging, to construct surrogates to deterministic computer models. However, outside of our own pilot work (Burl et al, 2006), there has been little effort directed toward using active learning to efficiently explore simulations that provide binary-valued outputs.…”

Section: Related Workmentioning

confidence: 99%

Active learning for directed exploration of complex systems

Burl

Wang

2009

Proceedings of the 26th Annual International Conference on Machine Learning

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Physics-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 ksteps along each dimension requires k d simulation trials (translating into k 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.

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Automated Knowledge Discovery from Simulators

Cited by 11 publications

References 0 publications

Size–frequency distributions of fragments from SPH/N-body simulations of asteroid impacts: Comparison with observed asteroid families

Size–frequency distributions of fragments from SPH/N-body simulations of asteroid impacts: Comparison with observed asteroid families

Towards assisted input and output data analysis in manufacturing simulation: The EDASim approach

Active learning for directed exploration of complex systems

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