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Published September 2025 | Version Published
Journal Article Open

Resolvent4py: A parallel Python package for analysis, model reduction and control of large-scale linear systems

Creators

  • 1. ROR icon University of Illinois Urbana-Champaign
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon Princeton University

Abstract

In this paper, we present resolvent4py, a parallel Python package for the analysis, model reduction and control of large-scale linear systems with millions or billions of degrees of freedom. This package provides the user with a friendly Python-like experience (akin to that of well-established libraries such as numpy and scipy), while enabling MPI-based parallelism through mpi4py, petsc4py and slepc4py. In turn, this allows for the development of streamlined and efficient Python code that can be used to solve several problems in fluid mechanics, solid mechanics, graph theory, molecular dynamics and several other fields.

Copyright and License

© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Acknowledgement

This material is based upon work supported by the National Science Foundation, United States under Grant No. 2139536, issued to the University of Illinois at Urbana-Champaign by the Texas Advanced Computing Center under subaward UTAUS-SUB00000545 with Dr. Daniel Stanzione as the PI. DB gratefully acknowledges support from the Office of Naval Research, United States (N00014-21-1-2256), and CR gratefully acknowledges support from the Air Force Office of Scientific Research, United States (FA9550-19-1-0005). TC and VA gratefully acknowledge support from The Boeing Company, United States (CT-BA-GTA-1) and the Office of Naval Research, United States (N00014-25-1-2072). BV was supported by the LDRD Program at Sandia National Laboratories, United States . Sandia is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. The computations in Section 3.1 were performed on TACC’s Stampede3 under ACCESS allocation CTS090004.

Contributions

Alberto Padovan: Writing – original draft, Validation, Software, Conceptualization. Vishal Anantharaman: Writing – original draft, Validation, Software. Clarence W. Rowley: Writing – review & editing, Software, Funding acquisition. Blaine Vollmer: Writing – review & editing, Software. Tim Colonius: Writing – review & editing, Funding acquisition. Daniel J. Bodony: Writing – review & editing, Funding acquisition.

Conflict of Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Clarence W. Rowley reports financial support was provided by Air Force Office of Scientific Research. Daniel J. Bodony reports financial support was provided by Office of Naval Research. Tim Colonius reports financial support was provided by Office of Naval Research. Tim Colonius reports financial support was provided by The Boeing Company. Daniel J. Bodony reports financial support was provided by National Science Foundation. Daniel J. Bodony reports financial support was provided by Sandia National Laboratories. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Additional details

Related works

Is new version of
Discussion Paper: arXiv:2506.20539 (arXiv)
Is supplemented by
Software: https://github.com/albertopadovan/resolvent4py (URL)

Funding

National Science Foundation
OAC-2139536
Texas Advanced Computing Center
UTAUS-SUB00000545
Office of Naval Research
N00014-21-1-2256
United States Air Force Office of Scientific Research
FA9550-19-1-0005
Boeing (United States)
CT-BA-GTA-1
Office of Naval Research
N00014-25-1-2072
United States Department of Energy
DE-NA0003525

Dates

Accepted
2025-07-20
Available
2025-08-09
Available online
Available
2025-08-09
Version of record

Caltech Custom Metadata

Caltech groups
Division of Engineering and Applied Science (EAS)
Publication Status
Published