Skip to main content
Springer Nature Link
Log in

DDDAS2022 Keynotes - Overview

  • Conference paper
  • First Online:
Dynamic Data Driven Applications Systems (DDDAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13984))

Included in the following conference series:

  • 428 Accesses

Abstract

The DDDAS2022 Conference featured five keynote presentations, and an invited talk, which addressed important science and technology topics, and provided examples of advances in capabilities enabled or supported by DDDAS-based methods. The presentations covered a ange of areas such as: aerospace systems, cyber-security, bio-infomatics and genomics, and adverse environmental events. Together with the present overview, papers contributed by the keynote speakers are included in these proceedings. In addition, the keynotes’ slides are available at www.1dddas.org.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Darema, F., Blasch, E., Ravela, S., Aved, A. (eds.) Dynamic Data Driven Applications Systems: Third International Conference on DDDAS 2020, Boston, MA, USA, 2–4 October  (2020)

    Google Scholar 

  2. Blasch, E., Ravela, S., Aved, A. (eds.): Handbook of Dynamic Data Driven Applications Systems. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95504-9

    Book  Google Scholar 

  3. Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds.) Handbook of Dynamic Data Driven Applications Systems. Springer,  Cham (2022). https://doi.org/10.1007/978-3-319-95504-9

  4. Darema, F., Blasch, E.P., Ravela, S., Aved, A.J. (eds.) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham (2023). https://doi.org/10.1007/978-3-030-74568-4

  5. Korobenko, A., Hsu, M.-C., Bazilevs, Y.: A computational steering framework for large-scale composite structures: part i—parametric-based design and analysis. In: Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds.) Handbook of Dynamic Data Driven Applications Systems, vol. 1, pp. 163–180. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-74568-4_8

  6. Korobenko, A., Pigazzini, M., Deng, X., Bazilevs, Y.: Multiscale DDDAS framework for damage prediction in aerospace composite structures. In: Blasch, E., Ravela, S., Aved, A. (eds.) Handbook of Dynamic Data Driven Applications Systems, pp. 677–696. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95504-9_30

    Chapter  Google Scholar 

  7. Bazilevs, Y., Deng, X., Korobenko, A., Lanza di Scalea, F.,  Todd, M.D., Taylor, S.G.:  Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor sata. J. Appl. Mech. 82(9) (2015)

    Google Scholar 

  8. Korobenko, A., et al.: Dynamic-data-driven damage prediction in aerospace composite structures. In:  AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2016

    Google Scholar 

  9. Y. Bazilevs, J. Yan, X. Deng, X. et al. Computer Modeling of Wind Turbines: 2. Free-Surface FSI and Fatigue-Damage. Arch Computat Methods Eng 26, 1101–1115, 2019

    Google Scholar 

  10. Carlone, L., Axelrod, A., Karaman, S., Chowdhary, G.: Aided optimal search: data-driven target pursuit from on-demand delayed binary observations. In: Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds.) Handbook of Dynamic Data Driven Applications Systems: Volume 1, pp. 303–343. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-74568-4_14

    Chapter  Google Scholar 

  11. Carlone, L., Axelrod, A., Karaman, S., Chowdhary, G.:  Data-driven prediction of confidence for EVAR in time-varying datasets. In: Blasch, E.P., Darema, F.,  Ravela, S., Aved, A.J. (eds.), Handbook of Dynamic Data Driven Applications Systems, vol. 1, 2nd ed. pp 389–412. Springer (2022). https://doi.org/10.1007/978-3-030-74568-4_16

  12. Ryou, G., Tal, E., Karaman, S.: Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers. Inter. J. Robotics Res. 40(12–14), 1352–1369 (2021)

    Article  Google Scholar 

  13. Tal, E., Karaman, S.: Accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness. IEEE Trans. Control Syst. Technol. 29(3), 1203–1218 (2020)

    Article  Google Scholar 

  14. Tal, E.,  Ryou, G., Karaman, S.:  Aerobatic trajectory generation for a vtol fixed-wing aircraft using differential flatness. arXiv preprint arXiv:2207.03524, (2022)

  15. Ruzicka, W.B.: Single-cell dissection of schizophrenia reveals neurodevelopmental-synaptic axis and transcriptional resilience.  2020. Single-cell dissection of schizophrenia reveals neurodevelopmental-synaptic axis and transcriptional resilience | medRxiv

    Google Scholar 

  16. Alhajjar, E., Maxwell, P.,  Bastian, N.: Adversarial machine learning in network intrusion detection systems. Expert Syst. Appli. 186 (2021)

    Google Scholar 

  17. Levinson, R., Niemoeller, S., Nag, S., Ravindra, V.: Planning satellite swarm measurements for earth science models: comparing constraint processing and MILP methods. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 32, pp. 471–479 (2022)

    Google Scholar 

  18. Melebari, A., Nag, S., Ravindra, V., Moghaddam, M.:  Soil moisture retrieval from multi-instrument and multi-frequency simulated measurements in support of future earth observing systems.  In: International Geoscience and Remote Sensing Symposium, pp.  5594–5597 (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. InfoSymbiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  2. MOVEJ Analytics, Fairborn, OH, USA

    Erik Blasch

Authors
  1. Frederica Darema
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erik Blasch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frederica Darema .

Editor information

Editors and Affiliations

  1. MOVEJ Analytics, Fairborn, OH, USA

    Erik Blasch

  2. InfoSymbiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  3. Air Force Research Laboratories, Canastota, NY, USA

    Alex Aved

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Darema, F., Blasch, E. (2024). DDDAS2022 Keynotes - Overview. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-52670-1_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-52669-5

  • Online ISBN: 978-3-031-52670-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics