IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v466y2024ics0096300323006495.html
   My bibliography  Save this article

A physics-informed data-driven algorithm for ensemble forecast of complex turbulent systems

Author

Listed:
  • Chen, Nan
  • Qi, Di

Abstract

A new ensemble forecast algorithm, named the physics-informed data-driven algorithm with conditional Gaussian statistics (PIDD-CG), is developed to predict the probability density functions (PDFs) of complex turbulent systems with partial observations. The PIDD-CG algorithm integrates a unique multiscale statistical closure modeling strategy with a highly efficient nonlinear data assimilation scheme to create a mixture of conditional statistics. An effective data-driven modeling method is integrated with the dominant conditional statistics to serve as the forecast ensemble members that can significantly reduce the high computational cost of recovering high-dimensional PDFs. The multiscale features in the time evolution of these conditional statistics ensembles are effectively predicted by an appropriate combination of physics-informed analytic formulae and recurrent neural networks. An information metric is adopted as the loss function for the latter to more accurately capture the desirable turbulent features. The proposed algorithm displays effective forecasting performance in both the transient and statistical equilibrium non-Gaussian PDFs of strongly turbulent systems with intermittency, regime switching, and extreme events. It also facilitates the development of efficient statistical reduced-order models in recovering and predicting the large-scale coherent structures of a large group of multiscale complex systems.

Suggested Citation

  • Chen, Nan & Qi, Di, 2024. "A physics-informed data-driven algorithm for ensemble forecast of complex turbulent systems," Applied Mathematics and Computation, Elsevier, vol. 466(C).
  • Handle: RePEc:eee:apmaco:v:466:y:2024:i:c:s0096300323006495
    DOI: 10.1016/j.amc.2023.128480
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300323006495
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2023.128480?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sarah A Sheard & Ali Mostashari, 2009. "Principles of complex systems for systems engineering," Systems Engineering, John Wiley & Sons, vol. 12(4), pages 295-311, December.
    2. Christian L. E. Franzke & Terence J. O'Kane & Judith Berner & Paul D. Williams & Valerio Lucarini, 2015. "Stochastic climate theory and modeling," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 6(1), pages 63-78, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Naman Krishna Pande & Puneet Pasricha & Arun Kumar & Arvind Kumar Gupta, 2024. "European Option Pricing in Regime Switching Framework via Physics-Informed Residual Learning," Papers 2410.10474, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dawn Gilbert & Mike Yearworth, 2016. "Complexity in a Systems Engineering Organization: An Empirical Case Study," Systems Engineering, John Wiley & Sons, vol. 19(5), pages 422-435, September.
    2. Michael W. Grenn & Shahram Sarkani & Thomas Mazzuchi, 2014. "The Requirements Entropy Framework in Systems Engineering," Systems Engineering, John Wiley & Sons, vol. 17(4), pages 462-478, December.
    3. Mary J. Simpson & Joseph J. Simpson, 2010. "Formal, theoretical aspects of systems engineering: Comments on “Principles of Complex Systems for Systems Engineering” [Syst Eng 12 (2009), 295–311]," Systems Engineering, John Wiley & Sons, vol. 13(2), pages 204-207, June.
    4. A. Terry Bahill, 2012. "Diogenes, a process for identifying unintended consequences," Systems Engineering, John Wiley & Sons, vol. 15(3), pages 287-306, September.
    5. Thomas Walworth & Mike Yearworth & Laura Shrieves & Hillary Sillitto, 2016. "Estimating Project Performance through a System Dynamics Learning Model," Systems Engineering, John Wiley & Sons, vol. 19(4), pages 334-350, July.
    6. Blake Roberts & Thomas Mazzuchi & Shahram Sarkani, 2016. "Engineered Resilience for Complex Systems as a Predictor for Cost Overruns," Systems Engineering, John Wiley & Sons, vol. 19(2), pages 111-132, March.
    7. Christos Ellinas & Neil Allan & Anders Johansson, 2016. "Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective," Systems Engineering, John Wiley & Sons, vol. 19(3), pages 179-192, May.
    8. Pedro Parraguez & Steven Eppinger & Anja Maier, 2016. "Characterizing Design Process Interfaces as Organization Networks: Insights for Engineering Systems Management," Systems Engineering, John Wiley & Sons, vol. 19(2), pages 158-173, March.
    9. Golnaz Vakili & Foroozossadat Tabatabaee & Siavash Khorsandi, 2013. "Emergence of cooperation in peer‐to‐peer systems: A complex adaptive system approach," Systems Engineering, John Wiley & Sons, vol. 16(2), pages 213-223, June.
    10. Shimon Fridkin & Sigal Kordova, 2022. "Examining Criteria for Choosing Subcontractors for Complex and Multi-Systems Projects," Sustainability, MDPI, vol. 14(22), pages 1-16, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:466:y:2024:i:c:s0096300323006495. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.