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

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Title: Resolvent4py: A parallel Python package for analysis, model reduction and control of large-scale linear systems
Authors: Padovan, Alberto, Anantharaman, Vishal, Rowley, Clarence W., Vollmer, Blaine, Colonius, Tim, Bodony, Daniel J.
Source: SoftwareX, 31, 102286, (2025-09)
Publisher Information: Elsevier
Publication Year: 2025
Collection: Caltech Authors (California Institute of Technology)
Subject Terms: Python, Parallel computing, Model reduction, Resolvent analysis, Stability analysis, Harmonic resolvent analysis
Description: 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. ; © 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/ ) ; 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. ; 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 ...
Document Type: article in journal/newspaper
Language: English
Relation: https://arxiv.org/abs/arXiv:2506.20539; https://github.com/albertopadovan/resolvent4py; https://authors.library.caltech.edu/communities/caltechauthors/; https://doi.org/10.1016/j.softx.2025.102286
DOI: 10.1016/j.softx.2025.102286
Availability: https://doi.org/10.1016/j.softx.2025.102286
Rights: info:eu-repo/semantics/openAccess ; Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number: edsbas.528988AC
Database: BASE
Description
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. ; © 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/ ) ; 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. ; 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 ...
DOI:10.1016/j.softx.2025.102286