AI for Science and Data-driven Science

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  • Igarashi Yasuhiko
    Graduate School of Frontier Sciences, The University of Tokyo
  • Takenaka Hikaru
    Graduate School of Frontier Sciences, The University of Tokyo
  • Nagata Kenji
    Artificial Intelligence Research Center, AIST PRESTO, Japan Science and Technology Agency
  • Okada Masato
    Graduate School of Frontier Sciences, The University of Tokyo Artificial Intelligence Research Center, AIST

Bibliographic Information

Other Title
  • AI for Scienceとデータ駆動科学
  • フォーラム AI for Scienceとデータ駆動科学 : ベイズ計測とVMAの提案
  • フォーラム AI for Science ト データ クドウ カガク : ベイズ ケイソク ト VMA ノ テイアン
  • ―ベイズ計測とVMAの提案―
  • ―Proposal of Bayesian Sensing and VMA―

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Description

<p>In this paper, we discuss artificial intelligence (AI) for science and one of its approach, data-driven science. Based on the tri-level of data-driven science proposed as basic theory, we show how to move ahead on AI for science. As a key issue to address in AI for Science, we introduce data-science framework to integrate extensive numerical data obtained by large-scale computation simulation, such as the “Kei(京)” computer, and by large-scale measuring system, e.g. synchrotron radiation and quantum beam. First, we propose Bayesian sensing, which is formulated base on the Bayesian inference, and Virtual Measurement Analysis (VMA) for analysis of the instrument data. Next, we introduce an extraction of effective model from electronic structure calculation for analysis of the simulated data. Finally, we discuss the integration of large-scale simulated and measurement data by data-driven approach through the effective model. </p>

Journal

  • Ouyou toukeigaku

    Ouyou toukeigaku 45 (3), 75-86, 2016

    Japanese Society of Applied Statistics

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