Advanced Data Mining and Reverse Engineering Algorithms for Space Sciences

Details Advanced Data Mining and Reverse Engineering Algorithms for Space Sciences Advanced Data Mining and Reverse Engineering Algorithms for Space Sciences

Publishing date

Dec 2005

URL

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005agufmsm13a0310v&link_type=abstract

Journals

American Geophysical Union, Fall Meeting 2005, abstract #SM13A-0310

Science

Mathematics

Field

Logic

Additional info 3

0520 Data Analysis: Algorithms And Implementation, 0555 Neural Networks, Fuzzy Logic, Machine Learning, 2724 Magnetopause And Boundary Layers, 7924 Forecasting (2722)

Type

Scientific paper

Abstract

Progress in space physics has always been strongly dependent on analysis of in situ spacecraft measurements. However, the vast majority of spacecraft data go unexplored as most scientists have had to rely on visual inspection of the data as the main means of mining the data. With the upcoming multi-spacecraft NASA missions (THEMIS, MMS, etc.) the growing size of data promises to outpace the ability of scientists to analyze them. Here we present a new technique, called Relevant Input Processor Network (RIPNet) for space plasma applications. Our recent application of this technique to modeling of magnetopause has demonstrated superior performance metrics (speed, accuracy, etc.) compared to standard techniques. RIPNet also offers reverse engineering capability. By this we mean that the outcome of the algorithm (i.e., the predicted model) is an analytical function with proper dependencies on the input parameters rather than say a set of neural net connection as in artificial neural net. This is very useful as it allows easy dissemination of results to others. The examination of the equation can also yield information about the underlying physics and relative importance of various terms. We will illustrate this technique through several examples.

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