Add model card for TimesNet-Gen

README.md

@@ -0,0 +1,56 @@
+---
+license: mit
+pipeline_tag: other
+tags:
+- seismology
+- geophysics
+- time-series
+---
+
+# TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
+
+TimesNet-Gen is a time-domain conditional generator designed for site-specific strong ground motion synthesis from accelerometer records. It employs a latent bottleneck with station identity conditioning to capture local site influences on ground motion characteristics.
+
+- **Paper:** [TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation](https://huggingface.co/papers/2512.04694)
+- **GitHub Repository:** [brsylmz23/TimesNet-Gen](https://github.com/brsylmz23/TimesNet-Gen)
+- **Demo:** [Hugging Face Space](https://huggingface.co/spaces/Barisylmz/TimesNet-Gen)
+
+## Installation
+
+To use the model, first clone the repository and install the required dependencies:
+
+```bash
+git clone https://github.com/brsylmz23/TimesNet-Gen.git
+cd TimesNet-Gen
+pip install -r requirements.txt
+```
+
+## Sample Usage
+
+After downloading the pre-trained checkpoints (as described in the [GitHub README](https://github.com/brsylmz23/TimesNet-Gen#2-download-pre-trained-model)), you can generate samples using the provided script:
+
+```bash
+# Generate 50 samples per station for the default stations
+python generate_samples.py --num_samples 50
+
+# Generate for specific stations (e.g., station IDs 0205 and 1716)
+python generate_samples.py --stations 0205 1716 --num_samples 100
+```
+
+The output includes generated waveforms in NPZ format along with HVSR curves and f₀ distribution comparisons.
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@misc{yilmaz2025timesnetgendeeplearningbasedsite,
+      title={TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation}, 
+      author={Baris Yilmaz and Bevan Deniz Cilgin and Erdem Akagündüz and Salih Tileylioglu},
+      year={2025},
+      eprint={2512.04694},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2512.04694}, 
+}
+```