Dynamic data-driven environmental sy...
Clark Conference ((2005 :)

 

  • Dynamic data-driven environmental systems science[electronic resource] :first International Conference, DyDESS 2014, Cambridge, MA, USA, November 5-7, 2014 : revised selected papers /
  • Record Type: Language materials, printed : Monograph/item
    [NT 15000414]: 005.7
    Title/Author: Dynamic data-driven environmental systems science : first International Conference, DyDESS 2014, Cambridge, MA, USA, November 5-7, 2014 : revised selected papers // edited by Sai Ravela, Adrian Sandu.
    remainder title: DyDESS 2014
    other author: Ravela, Sai.
    corporate name: Clark Conference
    Published: Cham : : Springer International Publishing :, 2015.
    Description: xi, 360 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    Subject: Databases
    Subject: Environmental sciences
    Subject: Computer science.
    Subject: Computer networks.
    Subject: Software engineering.
    Subject: Algorithms.
    Subject: User interfaces (Computer systems)
    Subject: Artificial intelligence.
    Subject: Information Systems Applications (incl. Internet)
    Subject: Algorithm Analysis and Problem Complexity.
    Subject: Computer Communication Networks.
    Subject: Artificial Intelligence (incl. Robotics)
    Subject: User Interfaces and Human Computer Interaction.
    ISBN: 9783319251387
    ISBN: 9783319251370
    [NT 15000228]: Sensing -- Environmental applications -- Reduced representations and features -- data assimilation and uncertainty quantification -- Planning and adaptive observation.
    [NT 15000229]: This book constitutes the refereed proceedings of the First International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014, held in Cambridge, MA, USA, in November 2014. The 24 revised full papers and 7 short papers were carefully reviewed and selected from 62 submissions and cover topics on sensing, imaging and retrieval for the oceans, atmosphere, space, land, earth and planets that is informed by the environmental context; algorithms for modeling and simulation, downscaling, model reduction, data assimilation, uncertainty quantification and statistical learning; methodologies for planning and control, sampling and adaptive observation, and efficient coupling of these algorithms into information-gathering and observing system designs; and applications of methodology to environmental estimation, analysis and prediction including climate, natural hazards, oceans, cryosphere, atmosphere, land, space, earth and planets.
    Online resource: http://dx.doi.org/10.1007/978-3-319-25138-7
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