Add task category and BibTeX citation

README.md

@@ -1,13 +1,19 @@
 ---
 language:
-    - en
-arxiv:
-    - 2506.08604
-license:
-    - mit
+- en
+license: mit
+arxiv: 2506.08604
+task_categories:
+- other
+tags:
+- physics
+- pde
+- flow-matching
+- scientific-machine-learning
 ---
 
-# [ICLR2026] Flow Matching Meets PDEs: A Unified Framework for Physics-Constrained Generation
+# Physics vs Distributions: Pareto Optimal Flow Matching with Physics Constraints
+
 <div>
   
   [![arXiv](https://img.shields.io/badge/arXiv-2506.08604-b31b1b.svg)](https://arxiv.org/abs/2506.08604)
@@ -15,9 +21,7 @@ license:
 
 </div>
 
-The datasets contain the data for the **dynamic stall** and **Kolmogorov flow** cases.
-
-
+This repository contains the datasets for the **dynamic stall** and **Kolmogorov flow** cases presented in the paper "[Physics vs Distributions: Pareto Optimal Flow Matching with Physics Constraints](https://huggingface.co/papers/2506.08604)".
 
 ## Dynamic Stall dataset
 The design space is defined as a four-dimensional hypercube. The design variables are:
@@ -66,4 +70,16 @@ Each simulation that corresponds to a dataset sample has 2 fields of size $128 \
 
 Each `hdf5` file contains two arrays:
 - `fields` with shape `(conditions, samples per condition, fields, x, y)`
-- `reynolds` with shape `(reynolds numbers, )`
+- `reynolds` with shape `(reynolds numbers, )`
+
+## Citation
+
+```bibtex
+@inproceedings{pbfm2026,
+    title={Physics vs Distributions: Pareto Optimal Flow Matching with Physics Constraints},
+    author={Giacomo Baldan and Qiang Liu and Alberto Guardone and Nils Thuerey},
+    booktitle={The Fourteenth International Conference on Learning Representations},
+    year={2026},
+    url={https://openreview.net/forum?id=tAf1KI3d4X}
+}
+```