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Intelligent simulation tools for mining large scientific data sets

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Abstract

This paper describes problems, challenges, and opportunities forintelligent simulation of physical systems. Prototype intelligent simulation tools have been constructed for interpreting massive data sets from physical fields and for designing engineering systems. We identify the characteristics of intelligent simulation and describe several concrete application examples. These applications, which include weather data interpretation, distributed control optimization, and spatio-temporal diffusion-reaction pattern analysis, demonstrate that intelligent simulation tools are indispensable for the rapid prototyping of application programs in many challenging scientific and engineering domains.

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Author information

Authors and Affiliations

  1. Xerox Palo Alto Research Center, 3333 Coyote Hill Road, 94304, Palo Alto, CA

    Feng Zhao

  2. Dartmouth College, 6211 Sudikoff Laboratory, 03755, Hanover, NH

    Chris Bailey-Kellogg

  3. The Ohio State University, 2015 Neil Avenue, 43210, Columbus, OH

    Xingang Huang & Iván Ordóñez

Authors
  1. Feng Zhao
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  2. Chris Bailey-Kellogg
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  3. Xingang Huang
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  4. Iván Ordóñez
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Additional information

Feng Zhao, Ph.D.: He leads Smart Matter Diagnostics Project at Xerox PARC. He has also been an Associate Professor of Computer and Information Science at Ohio State University and leads the Intelligent Simulation Group. His research interests include artificial intelligence, scientific computing, hybrid systems, and applications to problems in nonlinear dynamics and control. Zhao received PhD and MS in Electrical Engineering and Computer Science from M.I.T. At Xeron PARC and Ohio State, he led the development of Spatial Aggregation Technology and a software tool SAL to support data modeling and optimization for distributed applications. He is an Alfred P. Sloan Research Fellow and received the NSF and ONR Young Investigator Awards. He has authored or co-authored about 50 technical papers.

Chris Bailey-Kellogg, Ph.D.: He is a postdoc in Prof. Bruce Donald’s lab at Dartmouth, where he is applying intelligent simulation-like techniques to computational molecular biology problems, including automated interpretation of protein NMR data and computational drug design. He conducted his PhD research at Ohio State and Xerox PARC with Prof. Feng Zhao. For his dissertation, he implemented the Spatial Aggregation Language interpreter and used it to develop novel algorithms for decentralized control design for distributed parameter systems. He enjoys building tools that mix techniques from artificial intelligence, geometric reasoning, and scientific computation, in support of science and engineering applications.

Xingang Huang: He received a B.S. degree in computer science from Huazhong University of Science and Technology (China) in 1992, and a M.S. degree in computer science from the Institute of Computing Technology, Academia Sinica (China). Mr. Huang joined the Department of Computer and Information Science at the Ohio State University in 1995 and is now a Ph.D. candidate and research assistant. He is a member of AAAI. His research interests are intelligent data analysis, spatial reasoning and computer vision.

Iván Ordóñez-Reinoso: He was born in 1965 in Guayaquil, Ecuador. He holds a Professional degree in Electrical Engineering from the National Polytechnic School (Quito, Ecuador) and a Master’s degree in Computer Science from the Monterrey Institute of Technology (Monterrey, Mexico). He is currently a Ph. D. student at the Ohio State University, CIS Department. His research interests include spatiotemporal reasoning and morphogenesis in dynamical fields.

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Zhao, F., Bailey-Kellogg, C., Huang, X. et al. Intelligent simulation tools for mining large scientific data sets. NGCO 17, 333–347 (1999). https://doi.org/10.1007/BF03037240

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  • DOI: https://doi.org/10.1007/BF03037240

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