Informative Ensemble Kalman Learning for Neural Structure
We characterize the stochastic dynamics of Neural Learning to develop Informative Ensemble Kalman Learning. Specifically, an adaptive Ensemble Kalman Filter replaces backpropagation to quantify uncertainty and maximize information gain during Learning. De
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Frederica Darema Erik Blasch Sai Ravela Alex Aved (Eds.)
Dynamic Data Driven Application Systems Third International Conference, DDDAS 2020 Boston, MA, USA, October 2–4, 2020 Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7407
Frederica Darema Erik Blasch Sai Ravela Alex Aved (Eds.) •
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Dynamic Data Driven Application Systems Third International Conference, DDDAS 2020 Boston, MA, USA, October 2–4, 2020 Proceedings
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Editors Frederica Darema InfoSybiotic Systems Society Bethesda, MD, USA
Erik Blasch Air Force Office of Scientific Research Arlington, VA, USA
Sai Ravela Massachusetts Institute of Technology Cambridge, MA, USA
Alex Aved Air Force Research Laboratories Rome, NY, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-61724-0 ISBN 978-3-030-61725-7 (eBook) https://doi.org/10.1007/978-3-030-61725-7 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2020 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, expressed 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
Preface
The Dynamic Data Driven Applications Systems (DDDAS 2020) conference showcases scientific research advances and technology capabilities stemming from the DDDAS paradigm, whereby instrumenta