Add neuralUQ

README.md

@@ -34,6 +34,7 @@ A [script](./ref_convert.py) for converting bibtex to the markdown used in this
 8. **Universal Differential Equations for Scientific Machine Learning**, *Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, Kirill Zubov, Rohit Supekar, Dominic Skinner, Ali Ramadhan, Alan Edelman*, arXiv:2001.04385 [cs, math, q-bio, stat], 2020. [[paper](https://arxiv.org/pdf/2001.04385.pdf)][[code](https://github.com/ChrisRackauckas/universal_differential_equations)]
 9. **NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations**, *Kirill Zubov, Zoe McCarthy, Yingbo Ma, Francesco Calisto, Valerio Pagliarino, Simone Azeglio, Luca Bottero, Emmanuel Luján, Valentin Sulzer, Ashutosh Bharambe, N Vinchhi, , Kaushik Balakrishnan, Devesh Upadhyay, Chris Rackauckas*, arXiv:2107.09443 [cs], 2021. [[paper](https://arxiv.org/pdf/2107.09443)][[code](https://github.com/SciML/NeuralPDE.jl)]
 10. **IDRLnet: A Physics-Informed Neural Network Library**, *Wei Peng, Jun Zhang, Weien Zhou, Xiaoyu Zhao, Wen Yao, Xiaoqian Chen*, arXiv:2107.04320 [cs, math], 2021. [[paper](https://arxiv.org/pdf/2107.04320.pdf)][[code](https://github.com/idrl-lab/idrlnet)]
+11. **NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators**, *Zongren Zou, Xuhui Meng, Apostolos F. Psaros, George Em Karniadakis*, **UNKNOWN_JOURNAL**, 2022. [[paper](http://arxiv.org/pdf/2208.11866.pdf)][[code](https://github.com/Crunch-UQ4MI/neuraluq)]
 
 ## Papers on PINN Models
 1. **Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations**, *M. Raissi, P. Perdikaris, G. E. Karniadakis*, Journal of Computational Physics, 2019. [[paper](https://www.sciencedirect.com/science/article/pii/S0021999118307125)]