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PatrickHenkel1 merged 7 commits into from
Oct 30, 2025
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Updated readme #28

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Updated readme
PatrickHenkel Oct 30, 2025
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Initial plan
Copilot Oct 30, 2025
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Fix missing space after '2025.' in README citation
Copilot Oct 30, 2025
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Merge pull request #29 from RWTH-EBC/copilot/sub-pr-28
PatrickHenkel1 Oct 30, 2025
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Apply suggestions from code review
PatrickHenkel1 Oct 30, 2025
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PatrickHenkel1 Oct 30, 2025
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Updated
PatrickHenkel Oct 30, 2025
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1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -7,3 +7,4 @@
/**.egg-info
**/.coverage
/docs/build
/venv
22 changes: 15 additions & 7 deletions README.md
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[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://rwth-ebc.github.io/physXAI/)
![Coverage](https://raw.githubusercontent.com/RWTH-EBC/physXAI/dc3f25cbff23c06eac5344978b431a2faf27aa1c/build/reports/coverage.svg)
[![DOI:10.2139/ssrn.5276153](http://img.shields.io/badge/DOI-10.2139/ssrn.5276153-0A3874.svg)](http://dx.doi.org/10.2139/ssrn.5276153)
[![DOI:10.1016/j.buildenv.2025.113640](https://img.shields.io/badge/DOI-10.1016%2Fj.buildenv.2025.113640-227BC0)](https://doi.org/10.1016/j.buildenv.2025.113640)


<div>
Expand All @@ -20,12 +20,19 @@ The toolbox aims to integrate physical knowledge into machine learning models to
If you have any questions regarding physXAI, feel free to contact us at ebc-tools@eonerc.rwth-aachen.de

## Installation
1. Create conda or virtual environment with Python 3.12:
1. Create virtual environment with Python 3.12 and activate it:
```
conda create -n <name_of_environment> python=3.12 pip
conda activate <name_of_environment>
python -m venv physXAI
```
2. Clone pyhsXAI git repo:
- **On Windows:**
```
physXAI\Scripts\activate.bat
```
- **On Unix/Linux/macOS:**
```
source physXAI/bin/activate
```
2. Clone physXAI git repo:
```
git clone https://github.com/RWTH-EBC/physXAI.git
```
Expand Down Expand Up @@ -79,7 +86,8 @@ The project is organized into the following directories:
- `evaluation`: Modules for evaluating model performance using various metrics.
- `utils`: Utility functions and helper scripts used across different parts of the toolbox.
- `docs`: Contains documentation for the project.
- `Archive`: Used for archiving older versions of the code, data, or models to keep the main project structure clean.
- `build`: Contains saved test reports.
- `unittests`: Contains unittests for physXAI.

## How to contribute to the development

Expand All @@ -91,7 +99,7 @@ Furthermore, you are welcome to contribute via Pull Requests.

To cite physXAI, please use the following paper:

> Henkel, Patrick and Roß, Simon and Rätz, Martin and Müller, Dirk, Monotonic Physics-Constrained Neural Networks for Model Predictive Control of Building Energy Systems. Available at SSRN: https://ssrn.com/abstract=5276153 or http://dx.doi.org/10.2139/ssrn.5276153
> Henkel, Patrick and Roß, Simon and Rätz, Martin and Müller, Dirk, Monotonic physics-constrained neural networks for model predictive control of building energy systems. 2025. Available at Building and Environment: https://doi.org/10.1016/j.buildenv.2025.113640

Copilot AI Oct 30, 2025

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The citation format is incomplete. It should include the journal name, volume, and page numbers. A more complete format would be: 'Henkel, Patrick and Roß, Simon and Rätz, Martin and Müller, Dirk, Monotonic physics-constrained neural networks for model predictive control of building energy systems. Building and Environment, 2025, 268, 113640. https://doi.org/10.1016/j.buildenv.2025.113640'

Suggested change
> Henkel, Patrick and Roß, Simon and Rätz, Martin and Müller, Dirk, Monotonic physics-constrained neural networks for model predictive control of building energy systems. 2025. Available at Building and Environment: https://doi.org/10.1016/j.buildenv.2025.113640
> Henkel, Patrick and Roß, Simon and Rätz, Martin and Müller, Dirk, Monotonic physics-constrained neural networks for model predictive control of building energy systems. Building and Environment, 2025, 268, 113640. https://doi.org/10.1016/j.buildenv.2025.113640

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## Copyright and license
This tool is released by RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate <br />
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