ODE solvers and adjoint sensitivity analysis in PyTorch.
Project description
The author of this package has not provided a project description
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
torchdiffeq-0.2.2.tar.gz
(27.9 kB
view details)
Built Distribution
File details
Details for the file torchdiffeq-0.2.2.tar.gz.
File metadata
- Download URL: torchdiffeq-0.2.2.tar.gz
- Upload date:
- Size: 27.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.1 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 02486c8efb005d5dca99a0bd4905c9d13d945f6e33bc23a99b2b008ccb824fa1 |
|
| MD5 | fb11b1f427facc168e2b43a797dc65b7 |
|
| BLAKE2b-256 | bf118f09b212183435b3ae1b3041585864531de3e1689bdd4b56f6429d5ad0d6 |
File details
Details for the file torchdiffeq-0.2.2-py3-none-any.whl.
File metadata
- Download URL: torchdiffeq-0.2.2-py3-none-any.whl
- Upload date:
- Size: 31.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.1 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | cd8cb409f5de02403087fc9dcf41da16fd2ef7366fba5e3449d96837d38309fe |
|
| MD5 | d8777c3b66d951fbe150ee465c788610 |
|
| BLAKE2b-256 | a2474765cdc75ba37fffc286bce1f45adb56b577c7e8658f3c748a73642bd076 |
