Skip to main content

Official Python bindings for the SUNDIALS suite of nonlinear and differential/algebraic equation solvers.

Project description

SUNDIALS: SUite of Nonlinear and DIfferential/ALgebraic equation Solvers

GitHub Release PyPI - Version track SUNDIALS downloads

Version 7.7.0 (Apr 2026)

Center for Applied Scientific Computing, Lawrence Livermore National Laboratory

SUNDIALS is a family of software packages providing robust and efficient time integrators and nonlinear solvers that can easily be incorporated into existing simulation codes. The library is primarily written in C with interfaces to C++, Fortran, and Python (beta version) and provides support for serial, threaded, distributed, and GPU accelerated computing. The packages are designed to require minimal information from the user, allow users to supply their own data structures underneath the packages, and enable interfacing with user-supplied or third-party algebraic solvers and preconditioners.

The SUNDIALS suite consists of the following packages for ordinary differential equation (ODE) systems, differential-algebraic equation (DAE) systems, and nonlinear algebraic systems:

  • ARKODE - for integrating stiff, nonstiff, and multirate ODEs of the form

    $$M(t) y' = f_1(t,y) + f_2(t,y), \quad y(t_0) = y_0$$

  • CVODE - for integrating stiff and nonstiff ODEs of the form

    $$y' = f(t,y), \quad y(t_0) = y_0$$

  • CVODES - for integrating and sensitivity analysis (forward and adjoint) of ODEs of the form

    $$y' = f(t,y,p), \quad y(t_0) = y_0(p)$$

  • IDA - for integrating DAEs of the form

    $$F(t,y,y') = 0, \quad y(t_0) = y_0, \quad y'(t_0) = y_0'$$

  • IDAS - for integrating and sensitivity analysis (forward and adjoint) of DAEs of the form

    $$F(t,y,y',p) = 0, \quad y(t_0) = y_0(p), \quad y'(t_0) = y_0'(p)$$

  • KINSOL - for solving nonlinear algebraic systems of the form

    $$F(u) = 0 \quad \text{or} \quad G(u) = u$$

Installation

For installation directions, see the getting started section in the online documentation. In the released tarballs, installation directions are also available in INSTALL_GUIDE.pdf and the installation chapter of the user guides in the doc directory.

Warning to users who receive more than one of the individual packages at different times: Mixing old and new versions of SUNDIALS may fail. To avoid such failures, obtain all desired package at the same time.

Support

Full user guides for all of the SUNDIALS packages are available online. In the released tarballs, the doc directory includes PDFs of the user guides and documentation for the example programs. The example program documentation PDFs are also available on the releases page.

For information on recent changes to SUNDIALS see the CHANGELOG or the introduction chapter of any package user guide.

A list of Frequently Asked Questions on build and installation procedures as well as common usage issues is available on the SUNDIALS FAQ. For dealing with systems with nonphysical solutions or discontinuities see the SUNDIALS usage notes.

If you have a question not covered in the FAQ or usage notes, please submit your question as a GitHub issue or to the SUNDIALS mailing list.

Contributing

Bug fixes or minor changes are preferred via a pull request to the SUNDIALS GitHub repository. For more information on contributing see the CONTRIBUTING file.

Citing

See the online documentation or CITATIONS file for information on how to cite SUNDIALS in any publications reporting work done using SUNDIALS packages.

Authors

The SUNDIALS library has been developed over many years by a number of contributors. The current SUNDIALS team consists of Cody J. Balos, David J. Gardner, Alan C. Hindmarsh, Daniel R. Reynolds, Steven B. Roberts, and Carol S. Woodward. We thank Radu Serban for significant and critical past contributions.

Other contributors to SUNDIALS include: Mustafa Aggul, James Almgren-Bell, Lawrence E. Banks, Peter N. Brown, George Byrne, Rujeko Chinomona, Scott D. Cohen, Aaron Collier, Keith E. Grant, Steven L. Lee, Shelby L. Lockhart, John Loffeld, Daniel McGreer, Yu Pan, Slaven Peles, Cosmin Petra, H. Hunter Schwartz, Jean M. Sexton, Dan Shumaker, Steve G. Smith, Shahbaj Sohal, Allan G. Taylor, Hilari C. Tiedeman, Chris White, Ting Yan, and Ulrike M. Yang.

Acknowledgements

This material is based on work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program via the Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) Institute under DOE awards DE-AC52-07NA27344 and DE-SC-0021354.

This material is also based on work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Next-Generation Scientific Software Technologies program under contract DE-AC52-07NA27344. Additional support is also provided by SciDAC partnerships with the U.S. Department of Energy’s FES, NP, BES, OE, and BER offices as well as the LLNL Institutional Scientific Capability Portfolio.

License

SUNDIALS is released under the BSD 3-clause license. See the LICENSE and NOTICE files for details. All new contributions must be made under the BSD 3-clause license.

Please Note If you are using SUNDIALS with any third party libraries linked in (e.g., LAPACK, KLU, SuperLU_MT, PETSc, hypre, etc.), be sure to review the respective license of the package as that license may have more restrictive terms than the SUNDIALS license.

SPDX-License-Identifier: BSD-3-Clause

LLNL-CODE-667205  (ARKODE)
UCRL-CODE-155951  (CVODE)
UCRL-CODE-155950  (CVODES)
UCRL-CODE-155952  (IDA)
UCRL-CODE-237203  (IDAS)
LLNL-CODE-665877  (KINSOL)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

sundials4py-7.7.0-cp314-cp314t-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.14tWindows x86-64

sundials4py-7.7.0-cp314-cp314t-win32.whl (4.2 MB view details)

Uploaded CPython 3.14tWindows x86

sundials4py-7.7.0-cp314-cp314t-musllinux_1_2_x86_64.whl (15.1 MB view details)

Uploaded CPython 3.14tmusllinux: musl 1.2+ x86-64

sundials4py-7.7.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (15.0 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

sundials4py-7.7.0-cp314-cp314t-macosx_11_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.14tmacOS 11.0+ ARM64

sundials4py-7.7.0-cp314-cp314t-macosx_10_15_x86_64.whl (4.6 MB view details)

Uploaded CPython 3.14tmacOS 10.15+ x86-64

sundials4py-7.7.0-cp312-abi3-win_amd64.whl (4.6 MB view details)

Uploaded CPython 3.12+Windows x86-64

sundials4py-7.7.0-cp312-abi3-win32.whl (4.1 MB view details)

Uploaded CPython 3.12+Windows x86

sundials4py-7.7.0-cp312-abi3-musllinux_1_2_x86_64.whl (14.9 MB view details)

Uploaded CPython 3.12+musllinux: musl 1.2+ x86-64

sundials4py-7.7.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (14.7 MB view details)

Uploaded CPython 3.12+manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

sundials4py-7.7.0-cp312-abi3-macosx_11_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.12+macOS 11.0+ ARM64

sundials4py-7.7.0-cp312-abi3-macosx_10_15_x86_64.whl (4.6 MB view details)

Uploaded CPython 3.12+macOS 10.15+ x86-64

File details

Details for the file sundials4py-7.7.0-cp314-cp314t-win_amd64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp314-cp314t-win_amd64.whl
Algorithm Hash digest
SHA256 0ab9d93d9aed7f510afed5ac9bac78397a3320a9cfba42248d4e931e19833c35
MD5 b9e142adcd671e4576ffa67c88e6745b
BLAKE2b-256 83a5b86cfafdbffb6aae032615dbeee3fe2c3d316b59e98d5290ea13d2b0897f

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp314-cp314t-win32.whl.

File metadata

  • Download URL: sundials4py-7.7.0-cp314-cp314t-win32.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.14t, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for sundials4py-7.7.0-cp314-cp314t-win32.whl
Algorithm Hash digest
SHA256 763affb7ae1dff510d1f67b74cb5465ed5124aa502b9311f71d86955e36942eb
MD5 8ee59581974ac3c70c47b93b5f4f7da3
BLAKE2b-256 8425b89c0499802cee79a87c0094eb91b24a5a5400808e6b39daddda6144fece

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp314-cp314t-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp314-cp314t-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 af7a5b56f7facd1f74d7f62c26b4c67b68e25d3f908f49374fc75ed45b3aac85
MD5 b0d2072156d4d8e96870076630d47138
BLAKE2b-256 adfb190a49ca272f82b6a12f92b81ecc5ba572a469539558ad0bfaca8b2f4f31

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a6d85da6e572ddcaf446cb469dac592f022948fef0b61cbe9301226308a6ac9a
MD5 3ec4f9f75690fb1c3f620e58ad0885ee
BLAKE2b-256 09289cb562c3e0515921a5273d332197d95b1d9edcf190064d9cd0a67cd6a4a5

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp314-cp314t-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp314-cp314t-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b920c97efa5744787dfb2b6d3b792490f0c9762404e2f6340cfefbbe1dd5c484
MD5 30d38b7b17c2f3bdb7b11ea12281cbc0
BLAKE2b-256 27f23dc442b881a23519c487cd5d3bb26d52febfeb6c1ca1449e83198f621903

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp314-cp314t-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp314-cp314t-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 192f8b320687cb365bfd4e1de5e82da7370ef3ad22a778b2ca3eb5a6745f8e3a
MD5 4dca824c879f23c6905bf85a3078e315
BLAKE2b-256 528a8f1991de27ab69eb061524d84760d11baa55dd19a93fa0b65795e81fa558

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp312-abi3-win_amd64.whl.

File metadata

  • Download URL: sundials4py-7.7.0-cp312-abi3-win_amd64.whl
  • Upload date:
  • Size: 4.6 MB
  • Tags: CPython 3.12+, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for sundials4py-7.7.0-cp312-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 46d7d5da5e66fc9fcb303fa62e1bbd1e860d30be13f0cc4b901cf336cbb0e325
MD5 04b9c4189832de4c72e329a913b74c17
BLAKE2b-256 bd03a9fe9fb6c834e4b1570a8e5d562aabb1f8defc5ef93ac8b6df665fe8c483

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp312-abi3-win32.whl.

File metadata

  • Download URL: sundials4py-7.7.0-cp312-abi3-win32.whl
  • Upload date:
  • Size: 4.1 MB
  • Tags: CPython 3.12+, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for sundials4py-7.7.0-cp312-abi3-win32.whl
Algorithm Hash digest
SHA256 20f20b633b96c7a32d2d3ed52859d13faba8dd2b03b46b65f2216561d50808de
MD5 22a5c778d33882fd6b7c68e5ede81bf3
BLAKE2b-256 f8948d0929836c3387195ceebb89f688a5257b987e43229f2979178ea3d56e4d

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp312-abi3-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp312-abi3-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 e73cf63a864d2bd2fd6f95680ed7f7e500a1fb8e08b44099dc3ca11963545ca5
MD5 8c1cfdf06012cf9332baf4ec42e6ce13
BLAKE2b-256 57636320757090fdeec64716416196af5b5cf35525ef9c2efb662cf78cdb65b6

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 36ed6071c3e045f408d27f7a6ee01af5706e0d7cb697570cb93bd4cfe545b22b
MD5 78610ac017c2fd97ddbe578682e51da7
BLAKE2b-256 4290ae12a9a5182f4a4a9a4f9b8d9b1fa5503d238807c1df2e1142969ac502ac

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp312-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp312-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 63a38572e40313ab4774e65a1a18e2ea6a55ccaf62e1dcb9e1f1e93ed5d8fcde
MD5 e1c0ff87c925da0762b6068c27e768c5
BLAKE2b-256 30a19ec148408effdfe689bcde4e672f2e9da09ec8588f6a865a9735cbad75d3

See more details on using hashes here.

File details

Details for the file sundials4py-7.7.0-cp312-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for sundials4py-7.7.0-cp312-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 7271007d024b0eefefa672a9d8646165fa352b7c6f786e789c69e15e16a65728
MD5 89d91b45e91a88aeb83faac966ac9035
BLAKE2b-256 11fd56c06dd3c00456af7594c91d764c9f26157dfd6929fc87ecdb4d4912cb11

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page