Update doc

Author: lululxvi

README.md

@@ -18,11 +18,11 @@ DeepXDE is a deep learning library on top of [TensorFlow](https://www.tensorflow
 - approximates nonlinear operators via deep operator network (DeepONet),
 - approximates functions from a dataset with/without constraints.
 
-**Documentation**: [ReadTheDocs](https://deepxde.readthedocs.io/), [Extended abstract](http://ceur-ws.org/Vol-2587/article_14.pdf), [SIAM Rev.](https://doi.org/10.1137/19M1274067), [Slides](https://lululxvi.github.io/files/talks/2020SIAMMDS_MS70.pdf), [Video](https://www.youtube.com/watch?v=Wfgr1pMA9fY&list=PL1e3Jic2_DwwJQ528agJYMEpA0oMaDSA9&index=13)
+**Documentation**: [ReadTheDocs](https://deepxde.readthedocs.io/), [SIAM Rev.](https://doi.org/10.1137/19M1274067), [Slides](https://lululxvi.github.io/files/talks/2020SIAMMDS_MS70.pdf), [Video](https://www.youtube.com/watch?v=Wfgr1pMA9fY&list=PL1e3Jic2_DwwJQ528agJYMEpA0oMaDSA9&index=13)
 
 **Papers on algorithms**
 
-- Solving PDEs and IDEs via PINN: [Extended abstract](http://ceur-ws.org/Vol-2587/article_14.pdf), [SIAM Rev.](https://doi.org/10.1137/19M1274067)
+- Solving PDEs and IDEs via PINN: [SIAM Rev.](https://doi.org/10.1137/19M1274067)
 - Solving fPDEs via fPINN: [SIAM J. Sci. Comput.](https://epubs.siam.org/doi/abs/10.1137/18M1229845)
 - Solving stochastic PDEs via NN-arbitrary polynomial chaos (NN-aPC): [J. Comput. Phys.](https://www.sciencedirect.com/science/article/pii/S0021999119305340)
 - Learning from multi-fidelity data via MFNN: [PNAS](https://www.pnas.org/content/117/13/7052)

docs/index.rst

@@ -10,11 +10,11 @@ DeepXDE is a deep learning library on top of `TensorFlow <https://www.tensorflow
 - approximates nonlinear operators via deep operator network (DeepONet),
 - approximates functions from a dataset with/without constraints.
 
-**Documentation**: `ReadTheDocs <https://deepxde.readthedocs.io/>`_, `Extended abstract <http://ceur-ws.org/Vol-2587/article_14.pdf>`_, `Short paper <https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_2.pdf>`_, `Full paper <https://arxiv.org/abs/1907.04502>`_, `Slides <https://lululxvi.github.io/files/talks/2020SIAMMDS_MS70.pdf>`_, `Video <https://www.youtube.com/watch?v=Wfgr1pMA9fY&list=PL1e3Jic2_DwwJQ528agJYMEpA0oMaDSA9&index=13>`_
+**Documentation**: `ReadTheDocs <https://deepxde.readthedocs.io/>`_, `SIAM Rev. <https://doi.org/10.1137/19M1274067>`_, `Slides <https://lululxvi.github.io/files/talks/2020SIAMMDS_MS70.pdf>`_, `Video <https://www.youtube.com/watch?v=Wfgr1pMA9fY&list=PL1e3Jic2_DwwJQ528agJYMEpA0oMaDSA9&index=13>`_
 
 **Papers on algorithms**
 
-- Solving PDEs and IDEs via PINN: `Extended abstract <http://ceur-ws.org/Vol-2587/article_14.pdf>`_, `Short paper <https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_2.pdf>`_, `Full paper <https://arxiv.org/abs/1907.04502>`_
+- Solving PDEs and IDEs via PINN: `SIAM Rev. <https://doi.org/10.1137/19M1274067>`_
 - Solving fPDEs via fPINN: `SIAM J. Sci. Comput. <https://epubs.siam.org/doi/abs/10.1137/18M1229845>`_
 - Solving stochastic PDEs via NN-arbitrary polynomial chaos (NN-aPC): `J. Comput. Phys. <https://www.sciencedirect.com/science/article/pii/S0021999119305340>`_
 - Learning from multi-fidelity data via MFNN: `PNAS <https://www.pnas.org/content/117/13/7052>`_