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Award Abstract # 2028125
SWQU: Composable Next Generation Software Framework for Space Weather Data Assimilation and Uncertainty Quantification

NSF Org: PHY
Division Of Physics
Recipient: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Initial Amendment Date: August 28, 2020
Latest Amendment Date: August 18, 2022
Award Number: 2028125
Award Instrument: Continuing Grant
Program Manager: Vyacheslav (Slava) Lukin
vlukin@nsf.gov
 (703)292-7382
PHY
 Division Of Physics
MPS
 Directorate for Mathematical and Physical Sciences
Start Date: September 1, 2020
End Date: August 31, 2025 (Estimated)
Total Intended Award Amount: $3,100,000.00
Total Awarded Amount to Date: $3,100,000.00
Funds Obligated to Date: FY 2020 = $2,700,000.00
FY 2021 = $200,000.00

FY 2022 = $200,000.00
History of Investigator:
  • RICHARD LINARES (Principal Investigator)
    linaresr@mit.edu
  • Alan Edelman (Co-Principal Investigator)
  • Jaime Peraire (Co-Principal Investigator)
  • Philip Erickson (Co-Principal Investigator)
  • Boris Kramer (Co-Principal Investigator)
Recipient Sponsored Research Office: Massachusetts Institute of Technology
77 MASSACHUSETTS AVE
CAMBRIDGE
MA  US  02139-4301
(617)253-1000
Sponsor Congressional District: 07
Primary Place of Performance: Massachusetts Institute of Technology
77 Masssachusetts Avenue
Cambridge
MA  US  02139-4307
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): E2NYLCDML6V1
Parent UEI: E2NYLCDML6V1
NSF Program(s): OFFICE OF MULTIDISCIPLINARY AC,
COMPUTATIONAL MATHEMATICS,
AERONOMY,
CYBERINFRASTRUCTURE,
MSPA-INTERDISCIPLINARY,
Space Weather Research
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 026Z, 4444, 7231, 7569, 8092
Program Element Code(s): 125300, 127100, 152100, 723100, 745400, 808900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

This project seeks to develop the next generation of software for space weather modeling and prediction by bringing together experts in geospace sciences, uncertainty quantification, and software development, management, and sustainability. The electronic technologies that govern modern life are deeply dependent on satellite technologies such as the Global Positioning System (GPS), which helps us navigate cities and manages air-traffic all over the world. These satellites are also the de facto timing standard of technology and commerce, used to synchronize banking transactions worldwide, our smart-phones, and the internet. Yet, the accuracy and robustness of their signals are highly vulnerable to geospace disturbances. Satellite orbits have to be controlled precisely and to do so, geospace disturbances have to be predicted in advance. Space weather models with quantifiable predictive capability are the tools that are needed, and are presently largely absent, to continue to advance the satellite technologies and everything that depends on them. The composable software framework to be developed under this project will serve as a foundation that can be expanded on and improved over time, growing both the space weather prediction capabilities and the space weather modeling community.

Composable software is the crucible of computational science, allowing scientists to add their contribution to the numerical realm without having to repeat the work of others. The goal of this project is to build a next-generation framework for space weather uncertainty quantification and data assimilation, as the foundation of the growing body of computational tools for the field. The "must-haves" for this framework will be modern dispatch-based composability, reproducibility, ease-of-use, performance, portability, and extendability to today's and tomorrow's heterogeneous and novel architectures. This project will produce computationally scalable algorithms and open-source Julia-based software framework for data-driven models of space weather with the following properties: i) Composability: Software is composable when features and behaviors work together. For example, if uncertainty quantification can be applied to a program without a rewrite, the program and the uncertainty quantification compose; ii) Sustainability: Software is sustainable when the author of the program can leave the project, and new members of the project can maintain the software; iii) Portability: Software is portable when it can perform on heterogeneous hardware with a variety of underlying architectures; iv) Reproducibility: Software is reproducible today and into the future when a convenient and backward compatible pathway exists for users to readily examine, run, share, and modify code.

This award is made as a part of the joint NSF-NASA pilot program on Next Generation Software for Data-driven Models of Space Weather with Quantified Uncertainties (SWQU). It is supported by NSF Division of Atmospheric and Geospace Sciences, Division of Mathematical Sciences, Office of Advanced Cyberinfrastructure, and Office of Multidisciplinary Activities. All software developed as a result of this award will be made available by the awardee free of charge for non-commercial use; the software license will permit modification and redistribution of the software free of charge for non-commercial use.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Aa, Ercha and Zhang, ShunRong and Erickson, Philip J. and Wang, Wenbin and Coster, Anthea J. and Rideout, William "3D Regional Ionosphere Imaging and SED Reconstruction With a New TECBased Ionospheric Data Assimilation System (TIDAS)" Space Weather , v.20 , 2022 https://doi.org/10.1029/2022SW003055 Citation Details
DAmbrosio, Andrea and Servadio, Simone and Mun Siew, Peng and Linares, Richard "Novel SourceSink Model for Space Environment Evolution with Orbit Capacity Assessment" Journal of Spacecraft and Rockets , v.60 , 2023 https://doi.org/10.2514/1.A35579 Citation Details
Gondelach, David J. and Linares, Richard and Siew, Peng Mun "Atmospheric Density Uncertainty Quantification for Satellite Conjunction Assessment" Journal of Guidance, Control, and Dynamics , 2022 https://doi.org/10.2514/1.G006481 Citation Details
Issan, Opal and Kramer, Boris "Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference" Journal of Computational Physics , v.473 , 2023 https://doi.org/10.1016/j.jcp.2022.111689 Citation Details
Jain, Parikshit and McQuarrie, Shane and Kramer, Boris "Performance comparison of data-driven reduced models for a single-injector combustion process" AIAA Propulsion and Energy 2021 Forum , 2021 https://doi.org/10.2514/6.2021-3633 Citation Details
Vila-Pérez, Jordi and Van Heyningen, R. Loek and Nguyen, Ngoc-Cuong and Peraire, Jaume "Exasim: Generating discontinuous Galerkin codes for numerical solutions of partial differential equations on graphics processors" SoftwareX , v.20 , 2022 https://doi.org/10.1016/j.softx.2022.101212 Citation Details

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