diff --git a/README.md b/README.md
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@@ -143,4 +143,5 @@ A [script](./ref_convert.py) for converting bibtex to the markdown used in this
 10. **Characterizing possible failure modes in physics-informed neural networks**, *Aditi Krishnapriyan, Amir Gholami, Sh Zhe, ian, Robert Kirby, Michael W. Mahoney*, *NIPS*, 2021. [[paper](https://openreview.net/forum?id=a2Gr9gNFD-J)]
 11. **Convergence rate of DeepONets for learning operators arising from advection-diffusion equations**, *Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis*, arXiv:2102.10621 [math], 2021. [[paper](https://arxiv.org/abs/2102.10621)]
 12. **Estimates on the generalization error of physics-informed neural networks for approximating PDEs**, *Siddhartha Mishra, Roberto Molinaro*, IMA Journal of Numerical Analysis, 2022. [[paper](https://academic.oup.com/imajna/advance-article/doi/10.1093/imanum/drab093/6503953)]
-1. **Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)**, *Shamsulhaq Basir*, **arXiv:2209.09988v1[cs]**, **2022**. [[paper](https://arxiv.org/pdf/2209.09988.pdf)][[code](https://github.com/shamsbasir/investigating_mitigating_failure_modes_in_pinns)]
+13. **Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)**, *Shamsulhaq Basir*, **arXiv:2209.09988v1[cs]**, **2022**. [[paper](https://arxiv.org/pdf/2209.09988.pdf)][[code](https://github.com/shamsbasir/investigating_mitigating_failure_modes_in_pinns)]
+14. **Kolmogorov n–width and Lagrangian physics-informed neural networks: A causality-conforming manifold for convection-dominated PDEs**, *Rambod Mojgani, Maciej Balajewicz, Pedram Hassanzadeh*, Computer Methods in Applied Mechanics and Engineering, 2023. [[paper](https://www.sciencedirect.com/science/article/pii/S0045782522007666)][[code](https://github.com/rmojgani/LPINNs)]