diff --git a/README.md b/README.md
index 42b4297878950e9756bc971b389adf7a56b8a270..2c10b6bbf851073d49dc2b9542e11dd0418e304c 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 Python implementation of the 2L-VKOGA algorithm, which uses a kernel optimization (two layered kernel) before running VKOGA with the modified kernel.
 
 ## How to cite:
-If you use this code in your work, please cite the paper
+If you use this code in your work for scalar-valued output data, please cite the paper
 
 > T. Wenzel, F. Marchetti, and E. Perracchione. Data-driven kernel designs for
 optimized greedy schemes: A machine learning perspective.
@@ -19,5 +19,23 @@ arXiv preprint arXiv:2301.08047, 2023.
 }
 ```
 
+If you use this code in your work for vectorial-valued output data, please cite the paper
+
+> T. Wenzel, B. Haasdonk, H. Kleikamp, M. Ohlberger, and F. Schindler. Application
+of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Model-
+ing. arXiv preprint arXiv:2302.14526, 2023. Accepted for LSSC 2023 proceedings.
+
+```bibtex:
+@article{wenzel2023application,
+  title={Application of {D}eep {K}ernel {M}odels for {C}ertified
+and {A}daptive {RB}-{ML}-{ROM} {S}urrogate {M}odeling},
+  author={Wenzel, Tizian and Haasdonk, Bernard and Kleikamp, Hendrik and Ohlberger, Mario and Schindler, Felix},
+  journal={arXiv preprint arXiv:2302.14526},
+  year={2023},
+  note={Accepted for LSSC 2023 proceedings.}
+}
+```
+
+
 For further details on the VKOGA algorithm, please refer to [here](https://github.com/GabrieleSantin/VKOGA).