ODE solvers and adjoint sensitivity analysis in PyTorch.
Project description
The author of this package has not provided a project description
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
torchdiffeq-0.2.3.tar.gz
(29.3 kB
view details)
Built Distribution
File details
Details for the file torchdiffeq-0.2.3.tar.gz
.
File metadata
- Download URL: torchdiffeq-0.2.3.tar.gz
- Upload date:
- Size: 29.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.0 CPython/3.10.0
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | fe75f434b9090ac0c27702e02bed21472b0f87035be6581f51edc5d4013ea31a |
|
MD5 | d59132051fc4e73940f875e0558e5eae |
|
BLAKE2b-256 | 617e629146662e96da319fe237920b7d928a9832cbfa06c1d7ee8cdfe53ed450 |
File details
Details for the file torchdiffeq-0.2.3-py3-none-any.whl
.
File metadata
- Download URL: torchdiffeq-0.2.3-py3-none-any.whl
- Upload date:
- Size: 31.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.0 CPython/3.10.0
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b5b01ec1294a2d8d5f77e567bf17c5de1237c0573cb94deefa88326f0e18c338 |
|
MD5 | 35913a21177f1c6edc593bc399ae3a65 |
|
BLAKE2b-256 | 2c9bb9c3e17f261e30f630511390e0dd33fc529073f1f2db222a1f09dc49a1ae |