Handbook of Dynamic Data Driven Applications Systems

The Handbook of Dynamic Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies. Beginning with general concepts and

  • PDF / 34,123,856 Bytes
  • 734 Pages / 439.42 x 683.15 pts Page_size
  • 56 Downloads / 248 Views

DOWNLOAD

REPORT


ook of Dynamic Data Driven Applications Systems

Handbook of Dynamic Data Driven Applications Systems

Erik Blasch • Sai Ravela • Alex Aved Editors

Handbook of Dynamic Data Driven Applications Systems

123

Editors Erik Blasch Air Force Office of Scientific Research Air Force Research Laboratory Arlington, VA, USA

Sai Ravela Earth, Atmospheric and Planetary Sciences Massachusetts Institute of Technology Cambridge, MA, USA

Alex Aved Information Directorate Air Force Research Laboratory Rome, NY, USA

ISBN 978-3-319-95503-2 ISBN 978-3-319-95504-9 (eBook) https://doi.org/10.1007/978-3-319-95504-9 Library of Congress Control Number: 2018957614 © Springer Nature Switzerland AG 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1

Introduction to Dynamic Data Driven Applications Systems . . . . . . . . . . Erik Blasch, Dennis Bernstein, and Murali Rangaswamy

1

Part I Measurement-Aware: Data Assimilation, Uncertainty Quantification 2

3

4

Tractable Non-Gaussian Representations in Dynamic Data Driven Coherent Fluid Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sai Ravela Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hoong C. Yeong, Ryne Beeson, N. Sri Namachchivaya, Nicolas Perkowski, and Peter W. Sauer Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness . . . . . . . . . . . . . . . . . . . . Richard Linares, Vivek Vittaldev, and Humberto C. Godinez

29

47

75

Part II Signals-Aware: Process Monitoring 5

Towards Learning Spatio-Temporal Data Stream Relationships for Failu