Physics-Informed Modularized Neural Network for Building Energy Modeling
Project description
ModNN
ModNN is a Modularized Physics-Informed Neural Network for building energy modeling.
It incorporates with physics-informed model structure, loss function, and model constraints.
🚀 Installation
You can install the package using pip:
pip install modnn
🧠 Example
Please find the online Jupyter notebook for a step-by-step instruction: https://colab.research.google.com/drive/1A2jt1q53RtxGuaoym6N1PmlKELDPpYFX?usp=sharing
🧠 Update log
To further improve physics consistency, I replaced heattransfer module by energy balance equations, start from version 2.0.0
🧪 Requirements
Python 3.7+
PyTorch
NumPy
Pandas
Matplotlib
Seaborn
scikit-learn
tqdm
📬 License
MIT License
🙋♂️ Author
Zixin Jiang: zjiang19@syr.edu
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file modnn-2.0.1.tar.gz.
File metadata
- Download URL: modnn-2.0.1.tar.gz
- Upload date:
- Size: 27.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2e2a5d0c6657457b791f42c0ffd609be146afdba96e6a69c69e21e15ad76344c
|
|
| MD5 |
a8f48b61da84c5a1c600bd555441acb6
|
|
| BLAKE2b-256 |
1e69e13a08250bcc31459ced7db3f8c72f57919c99a2a6b39dae4b76c35bec28
|
File details
Details for the file modnn-2.0.1-py3-none-any.whl.
File metadata
- Download URL: modnn-2.0.1-py3-none-any.whl
- Upload date:
- Size: 29.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
71f47d7ad3b2040ad935a37db1821164ad8a0f54229f2bada697793b547fb84c
|
|
| MD5 |
980c7d782d68deec193296f0e1d234d6
|
|
| BLAKE2b-256 |
672ddc893016d5b154141037819063ec18c6b8108176da3df435f68416829d5f
|
