© 2015

Dynamic Data-Driven Environmental Systems Science

First International Conference, DyDESS 2014, Cambridge, MA, USA, November 5-7, 2014, Revised Selected Papers

  • Sai Ravela
  • Adrian Sandu
Conference proceedings DyDESS 2014

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8964)

Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 8964)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Sensing

    1. Front Matter
      Pages 1-1
    2. Ru-Shan Gao, James W. Elkins, Gregory J. Frost, Allison C. McComiskey, Fred L. Moore, Daniel M. Murphy et al.
      Pages 10-15
    3. Jesse Belden, Jonathon Pendlebury, Alexander Jafek, Tadd Truscott
      Pages 28-38
  3. Environmental Applications

    1. Front Matter
      Pages 39-39
    2. E. Ramona Stefanescu, Abani Patra, E. Bruce Pitman, Marcus Bursik, Puneet Singla, Tarunraj Singh
      Pages 41-53
    3. Carlos Brun, Ana Cortés, Tomàs Margalef
      Pages 54-67
    4. Dong-Jun Seo, Branko Kerkez, Michael Zink, Nick Fang, Jean Gao, Xinbao Yu
      Pages 68-78
    5. Craig C. Douglas, Tainara Mendes de Andrade Soares, Mauricío Vieira Kritz
      Pages 89-99
    6. JD Knapp, Matias Elo, James Shaeffer, Paul G. Flikkema
      Pages 100-111
  4. Reduced Representations and Features

    1. Front Matter
      Pages 113-113
    2. Charanraj Thimmisetty, Arman Khodabakhshnejad, Nima Jabbari, Fred Aminzadeh, Roger Ghanem, Kelly Rose et al.
      Pages 157-166
    3. Kian Hsiang Low, Jie Chen, Trong Nghia Hoang, Nuo Xu, Patrick Jaillet
      Pages 167-181

About these proceedings


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.


bayesian inference dynamic programming ensemble learning hybrid method simulation data uncertainty dynamic data-driven invariant manifolds level-set method middleware multi-objective assimilation path planning real-time reduced modeling risk analysis state estimation time-optimal uncertainty quantification volume reconstruction workflows

Editors and affiliations

  • Sai Ravela
    • 1
  • Adrian Sandu
    • 2
  1. 1.Massachusetts Inst. of TechnologyCambridgeUSA
  2. 2.VA Polytechnic Institute and State Univ.BlacksburgUSA

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