Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning
Using traditional computational fluid dynamics and aeroacoustics methods, the accurate simulation of aeroacoustic sources requires high compute resources to resolve all necessary physical phenomena. In contrast, once trained, artificial neural networks su
- PDF / 46,920,392 Bytes
- 388 Pages / 439.37 x 666.142 pts Page_size
- 15 Downloads / 193 Views
Heike Jagode Hartwig Anzt Guido Juckeland Hatem Ltaief (Eds.)
High Performance Computing ISC High Performance 2020 International Workshops Frankfurt, Germany, June 21–25, 2020 Revised Selected Papers
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
12321
More information about this series at http://www.springer.com/series/7407
Heike Jagode Hartwig Anzt Guido Juckeland Hatem Ltaief (Eds.) •
•
•
High Performance Computing ISC High Performance 2020 International Workshops Frankfurt, Germany, June 21–25, 2020 Revised Selected Papers
123
Editors Heike Jagode University of Tennessee at Knoxville Knowville, TN, USA Guido Juckeland Computational Science Helmholtz-Zentrum Dresden Rossendorf Dresden, Sachsen, Germany
Hartwig Anzt Department of Mathematics KIT für Technologie Karlsruhe Karlsruhe, Baden-Württemberg, Germany Hatem Ltaief Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal, Saudi Arabia
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-59850-1 ISBN 978-3-030-59851-8 (eBook) https://doi.org/10.1007/978-3-030-59851-8 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2020 Chapters 6, 19 and 24 are licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapters. 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 institution
