Update README.md

README.md

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----
-license: cc-by-4.0
----
-
-# BEAR-CFD-Dataset
-
-This dataset is released with paper **Data-driven operator learning for energy-efficient and indoor air quality-aware building ventilation control**. 
-
-Author: Yuexin Bian, Yuanyuan Shi
-
-The data was generated using **ANSYS FLUENT 2023R2** and includes both steady-state and transient flow simulations. It is designed to support research in scientific machine learning, particularly in learning neural operators and data-driven PDE solvers. 
-
-This dataset is released for research purposes only. Please cite the authors if used in published work.
-
-
-## 🧪 Dataset Description
-
-### Steady-State Simulations
-- **10 distinct cases** were simulated, each with unique boundary conditions.
-- Each simulation converges to a time-independent solution.
-- These cases represent equilibrium airflow behavior under varying physical setups.
-
-### Transient Simulations
-- **300 cases**, each initialized from a corresponding steady-state solution.
-- Each simulation spans **30 minutes** of physical time.
-- Simulations use a **time step size of 10 seconds** and output results every 30 seconds.
-- This results in **60 time steps per case**, capturing unsteady, time-varying airflow dynamics under changing boundary conditions.
-
-Transient simulation data is used to train neural operators for modeling the evolution of fluid dynamics in time.
-
-## 📂 Dataset Structure
-
-The dataset repository contains the following subfolders:
-
-├── raw_data/ 
-    # Transient simulation results in pickle format   
-├── processed_data/ # Processed datasets split into train/test sets   
-├── models/ # Trained neural operator models   
-├── steady_case_data/ # Original ANSYS steady-state result files
-
-
-- **Pickle Files (`.pkl`)**: Each transient simulation case is stored as a pickle file for convenient loading in Python.
-- **ANSYS Data**: The steady-state simulation results are in native ANSYS formats (e.g., `.cas.h5`, `.dat.h5`).
-
-## 📚 Use Cases
-
-- Training neural operators to learn the temporal dynamics of fluid flow.
-- Studying how steady-state solutions influence transient behavior.
-- Benchmarking data-driven PDE solvers and physics-informed machine learning models.
-
-## 📥 Loading Example
-
-```python
-import pickle
-
-with open("raw_data/unsteady_10.pkl", "rb") as f:
-    data = pickle.load(f)
-
-import ansys.fluent.core as pyfluent
-
-solver = pyfluent.launch_fluent(mode="solver", precision="double",processor_count=6)
-solver.file.read_case_data(file_name='steady_case_data/steady_seed0.cas.h5')
+---
+license: cc-by-4.0
+---
+
+# BEAR-CFD-Dataset
+
+This dataset is released with paper **Data-driven operator learning for energy-efficient and indoor air quality-aware building ventilation control**. 
+
+
+The data was generated using **ANSYS FLUENT 2023R2** and includes both steady-state and transient flow simulations. It is designed to support research in scientific machine learning, particularly in learning neural operators and data-driven PDE solvers. 
+This dataset is released for research purposes only. Please cite the authors if used in published work.
+
+### 📚 BibTeX
+```bibtex
+@article{bian2025data,
+  title={Data-driven operator learning for energy-efficient building control},
+  author={Bian, Yuexin and Shi, Yuanyuan},
+  journal={arXiv preprint arXiv:2504.21243},
+  year={2025}
+}
+```
+
+## 💻 Code Repository
+
+All code used for data generation, preprocessing, and baseline models is available on GitHub:  
+[🔗 GitHub Repository](https://github.com/alwaysbyx/BuildingControlCFD)
+
+## 🧪 Dataset Description
+
+### Steady-State Simulations
+- **10 distinct cases** were simulated, each with unique boundary conditions.
+- Each simulation converges to a time-independent solution.
+- These cases represent equilibrium airflow behavior under varying physical setups.
+
+### Transient Simulations
+- **300 cases**, each initialized from a corresponding steady-state solution.
+- Each simulation spans **30 minutes** of physical time.
+- Simulations use a **time step size of 10 seconds** and output results every 30 seconds.
+- This results in **60 time steps per case**, capturing unsteady, time-varying airflow dynamics under changing boundary conditions.
+
+Transient simulation data is used to train neural operators for modeling the evolution of fluid dynamics in time.
+
+## 📂 Dataset Structure
+
+The dataset repository contains the following subfolders:
+
+├── raw_data/ 
+    # Transient simulation results in pickle format   
+├── processed_data/ # Processed datasets split into train/test sets   
+├── models/ # Trained neural operator models   
+├── steady_case_data/ # Original ANSYS steady-state result files
+
+
+- **Pickle Files (`.pkl`)**: Each transient simulation case is stored as a pickle file for convenient loading in Python.
+- **ANSYS Data**: The steady-state simulation results are in native ANSYS formats (e.g., `.cas.h5`, `.dat.h5`).
+
+## 📚 Use Cases
+
+- Training neural operators to learn the temporal dynamics of fluid flow.
+- Studying how steady-state solutions influence transient behavior.
+- Benchmarking data-driven PDE solvers and physics-informed machine learning models.
+
+## 📥 Loading Example
+
+```python
+import pickle
+
+with open("raw_data/unsteady_10.pkl", "rb") as f:
+    data = pickle.load(f)
+
+import ansys.fluent.core as pyfluent
+
+solver = pyfluent.launch_fluent(mode="solver", precision="double",processor_count=6)
+solver.file.read_case_data(file_name='steady_case_data/steady_seed0.cas.h5')
 solver.settings.file.read_data(file_name = "steady_case_data/steady_seed0.dat.h5")