pina-mathlab


Namepina-mathlab JSON
Version 0.1.2.post2501 PyPI version JSON
download
home_pagehttps://github.com/mathLab/PINA
SummaryPhysic Informed Neural networks for Advance modeling.
upload_time2025-01-01 03:09:52
maintainerNone
docs_urlNone
authorPINA Contributors
requires_pythonNone
licenseMIT
keywords machine-learning deep-learning modeling pytorch ode neural-networks differential-equations pde hacktoberfest pinn physics-informed physics-informed-neural-networks neural-operators equation-learning lightining
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            PINA is a Python package providing an easy interface to deal with physics-informed neural networks (PINN) for the approximation of (differential, nonlinear, ...) functions. Based on Pytorch, PINA offers a simple and intuitive way to formalize a specific problem and solve it using PINN. The approximated solution of a differential equation can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface.

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/mathLab/PINA",
    "name": "pina-mathlab",
    "maintainer": null,
    "docs_url": null,
    "requires_python": null,
    "maintainer_email": null,
    "keywords": "machine-learning deep-learning modeling pytorch ode neural-networks differential-equations pde hacktoberfest pinn physics-informed physics-informed-neural-networks neural-operators equation-learning lightining",
    "author": "PINA Contributors",
    "author_email": "demo.nicola@gmail.com, dario.coscia@sissa.it",
    "download_url": "https://files.pythonhosted.org/packages/97/38/0b99de79095e78f218baee55afddbdbb95b1156e91c9821785a67177303c/pina-mathlab-0.1.2.post2501.tar.gz",
    "platform": null,
    "description": "PINA is a Python package providing an easy interface to deal with physics-informed neural networks (PINN) for the approximation of (differential, nonlinear, ...) functions. Based on Pytorch, PINA offers a simple and intuitive way to formalize a specific problem and solve it using PINN. The approximated solution of a differential equation can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface.\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "Physic Informed Neural networks for Advance modeling.",
    "version": "0.1.2.post2501",
    "project_urls": {
        "Homepage": "https://github.com/mathLab/PINA"
    },
    "split_keywords": [
        "machine-learning",
        "deep-learning",
        "modeling",
        "pytorch",
        "ode",
        "neural-networks",
        "differential-equations",
        "pde",
        "hacktoberfest",
        "pinn",
        "physics-informed",
        "physics-informed-neural-networks",
        "neural-operators",
        "equation-learning",
        "lightining"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "f71f1c788cfe30bb89ecace74c9bc0cdf957e32cb96b47cf62c5fc13d044100a",
                "md5": "c0c376774288b7e85b18331a4143b67e",
                "sha256": "5537e098c040b89361856c483dc4c49ef99b759658be04eb5789eebff0cf0590"
            },
            "downloads": -1,
            "filename": "pina_mathlab-0.1.2.post2501-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "c0c376774288b7e85b18331a4143b67e",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 135045,
            "upload_time": "2025-01-01T03:09:50",
            "upload_time_iso_8601": "2025-01-01T03:09:50.413478Z",
            "url": "https://files.pythonhosted.org/packages/f7/1f/1c788cfe30bb89ecace74c9bc0cdf957e32cb96b47cf62c5fc13d044100a/pina_mathlab-0.1.2.post2501-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "97380b99de79095e78f218baee55afddbdbb95b1156e91c9821785a67177303c",
                "md5": "bac66ca518da00485a786dafb9b374b8",
                "sha256": "fb2cd3a2b81654abb88686cd11142de6d317fbd82eac9888f336eb77532f7df1"
            },
            "downloads": -1,
            "filename": "pina-mathlab-0.1.2.post2501.tar.gz",
            "has_sig": false,
            "md5_digest": "bac66ca518da00485a786dafb9b374b8",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 97591,
            "upload_time": "2025-01-01T03:09:52",
            "upload_time_iso_8601": "2025-01-01T03:09:52.847183Z",
            "url": "https://files.pythonhosted.org/packages/97/38/0b99de79095e78f218baee55afddbdbb95b1156e91c9821785a67177303c/pina-mathlab-0.1.2.post2501.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-01-01 03:09:52",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "mathLab",
    "github_project": "PINA",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "pina-mathlab"
}
        
Elapsed time: 2.53590s