TEDEouS - Torch Exhaustive Differential Equation Solver
=======================================================
The purpose of the project
--------------------------
1. Make equation discovery more transparent and illustrative
2. Combine power of pytorch, numerical methods and math overall to conquer and solve ALL XDEs(X={O,P}). There are some examples to provide a little insight to an operator form
Table of Contents
--------------------
- `Core features <Core features_>`_
- `Installation <Installation_>`_
- `Examples <Examples_>`_
- `Project Structure <Project Structure_>`_
- `Documentation <Documentation_>`_
- `Getting started <Getting started_>`_
- `License <License_>`_
- `Contacts <Contacts_>`_
- `Citation <Citation_>`_
Core features
-------------
* Solve ODE initial- or boundary-value problems
* Solve PDE initial-boundary value problems
* Use variable models and their differentiation methods
* Faster solution using cache
Installation
------------
TEDEouS can be installed with ``pip``::
$ git clone https://github.com/ITMO-NSS-team/torch_DE_solver.git
$ cd torch_DE_solver
$ pip install -r requirements.txt
Examples
------------
After the TEDEouS is installed the user may refer to various examples that are in examples forlder. ::
$ cd examples
Every example is designed such that the boxplots of the launches are commented and the preliminary results are not shown, but stored in separate folders.
* Legendre polynomial equation
::
$ python example_ODE_Legendre.py
or ::
$ python example_ODE_Legendre_autograd.py
* Panleve transcendents (others are placed in 'examples\\to_renew' folder due to the architecture change)
::
$ python example_Painleve_I.py
* Wave equation (non-physical conditions for equation discovery problem)
::
$ python example_wave_paper_autograd.py
* Wave equation (initial-boundary value problem)
::
$ python example_wave_physics.py
* Heat equation
::
$ python example_heat.py
* KdV equation (non-physical conditions for equation discovery problem)
::
$ python example_KdV.py
* KdV equation (solitary solution with periodic boundary conditions)
::
$ python example_KdV_periodic.py
* Burgers equation and DeepXDE comparison
::
$ python example_Burgers_paper.py
Project Structure
-----------------
Stable version is located in the master branch.
Documentation
-------------
https://torch-de-solver.readthedocs.io/en/docs/index.html
Getting started
---------------
Schroedinger equation example step-by-step https://torch-de-solver.readthedocs.io/en/docs/tedeous/examples/schrodinger.html
License
-------
TEDEouS is distributed under BSD-3 licence found in LICENCE file
Contacts
--------
- Feel free to make issues or contact @SuperSashka directly
Citation
--------
::
@article{hvatov2023solver,
AUTHOR = {Hvatov, Alexander},
TITLE = {Automated Differential Equation Solver Based on the Parametric Approximation Optimization},
JOURNAL = {Mathematics},
VOLUME = {11},
YEAR = {2023},
NUMBER = {8},
ARTICLE-NUMBER = {1787},
URL = {https://www.mdpi.com/2227-7390/11/8/1787},
ISSN = {2227-7390},
DOI = {10.3390/math11081787}
}
=======================================================
The purpose of the project
--------------------------
1. Make equation discovery more transparent and illustrative
2. Combine power of pytorch, numerical methods and math overall to conquer and solve ALL XDEs(X={O,P}). There are some examples to provide a little insight to an operator form
Table of Contents
--------------------
- `Core features <Core features_>`_
- `Installation <Installation_>`_
- `Examples <Examples_>`_
- `Project Structure <Project Structure_>`_
- `Documentation <Documentation_>`_
- `Getting started <Getting started_>`_
- `License <License_>`_
- `Contacts <Contacts_>`_
- `Citation <Citation_>`_
Core features
-------------
* Solve ODE initial- or boundary-value problems
* Solve PDE initial-boundary value problems
* Use variable models and their differentiation methods
* Faster solution using cache
Installation
------------
TEDEouS can be installed with ``pip``::
$ git clone https://github.com/ITMO-NSS-team/torch_DE_solver.git
$ cd torch_DE_solver
$ pip install -r requirements.txt
Examples
------------
After the TEDEouS is installed the user may refer to various examples that are in examples forlder. ::
$ cd examples
Every example is designed such that the boxplots of the launches are commented and the preliminary results are not shown, but stored in separate folders.
* Legendre polynomial equation
::
$ python example_ODE_Legendre.py
or ::
$ python example_ODE_Legendre_autograd.py
* Panleve transcendents (others are placed in 'examples\\to_renew' folder due to the architecture change)
::
$ python example_Painleve_I.py
* Wave equation (non-physical conditions for equation discovery problem)
::
$ python example_wave_paper_autograd.py
* Wave equation (initial-boundary value problem)
::
$ python example_wave_physics.py
* Heat equation
::
$ python example_heat.py
* KdV equation (non-physical conditions for equation discovery problem)
::
$ python example_KdV.py
* KdV equation (solitary solution with periodic boundary conditions)
::
$ python example_KdV_periodic.py
* Burgers equation and DeepXDE comparison
::
$ python example_Burgers_paper.py
Project Structure
-----------------
Stable version is located in the master branch.
Documentation
-------------
https://torch-de-solver.readthedocs.io/en/docs/index.html
Getting started
---------------
Schroedinger equation example step-by-step https://torch-de-solver.readthedocs.io/en/docs/tedeous/examples/schrodinger.html
License
-------
TEDEouS is distributed under BSD-3 licence found in LICENCE file
Contacts
--------
- Feel free to make issues or contact @SuperSashka directly
Citation
--------
::
@article{hvatov2023solver,
AUTHOR = {Hvatov, Alexander},
TITLE = {Automated Differential Equation Solver Based on the Parametric Approximation Optimization},
JOURNAL = {Mathematics},
VOLUME = {11},
YEAR = {2023},
NUMBER = {8},
ARTICLE-NUMBER = {1787},
URL = {https://www.mdpi.com/2227-7390/11/8/1787},
ISSN = {2227-7390},
DOI = {10.3390/math11081787}
}
2 maintainers
Metadata
2 maintainers
Release files for TEDEouS 0.4.11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tedeous-0.4.11.tar.gz | 56.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tedeous-0.4.11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 125.3 kB
Release files / tedeous-0.4.11.tar.gz
| Download URL | tedeous-0.4.11.tar.gz |
|---|---|
| Size | 56.8 kB |
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|---|---|
| Size | 68.5 kB |
| Tags | Python 3 |
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