update bibliography

paper.bib

@@ -3,14 +3,16 @@ @book{doherty2010approaches
   author={Doherty, John E and Hunt, Randall J},
   volume={2010},
   year={2010},
-  publisher={US Department of the Interior, US Geological Survey Reston, VA, USA}
+  publisher={US Department of the Interior, US Geological Survey Reston, VA, USA},
+  doi={10.3133/sir20105169}
 }
 @techreport{mercer2020amanzi,
   title={Amanzi--ATS: Modeling Environmental Systems across Scales [Brief]},
   author={Mercer-Smith, Janet Anne},
   number={LA-UR-20-26636},
   year={2020},
-  institution={Los Alamos National Lab.(LANL), Los Alamos, NM (United States)}
+  institution={Los Alamos National Lab.(LANL), Los Alamos, NM (United States)},
+  doi={10.2172/1657092}
 }
 @article{greer2022comparison,
   title={A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks},
@@ -20,13 +22,19 @@ @article{greer2022comparison
   number={4},
   pages={e2021WR031188},
   year={2022},
-  publisher={Wiley Online Library}
+  publisher={Wiley Online Library},
+  doi={10.1029/2021wr031188}
 }
-@article{betancourt2017conceptual,
-  title={A conceptual introduction to Hamiltonian Monte Carlo},
-  author={Betancourt, Michael},
-  journal={arXiv preprint arXiv:1701.02434},
-  year={2017}
+@article{gelman2015stan,
+  title={Stan: A probabilistic programming language for Bayesian inference and optimization},
+  author={Gelman, Andrew and Lee, Daniel and Guo, Jiqiang},
+  journal={Journal of Educational and Behavioral Statistics},
+  volume={40},
+  number={5},
+  pages={530--543},
+  year={2015},
+  publisher={Sage Publications Sage CA: Los Angeles, CA},
+  doi={10.3102/10769986156061}
 }
 @article{wu2022inverse,
   title={Inverse analysis with variational autoencoders: a comparison of shallow and deep networks},
@@ -35,31 +43,39 @@ @article{wu2022inverse
   volume={3},
   number={2},
   year={2022},
-  publisher={Begel House Inc.}
+  publisher={Begel House Inc.},
+  doi={10.1615/jmachlearnmodelcomput.2022042093}
 }
 @article{pachalieva2022physics,
   title={Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management},
-  author={Pachalieva, Aleksandra and O'Malley, Daniel and Harp, Dylan Robert and Viswanathan, Hari},
-  journal={arXiv e-prints},
-  pages={arXiv--2206},
-  year={2022}
+  author={Pachalieva, Aleksandra and O’Malley, Daniel and Harp, Dylan Robert and Viswanathan, Hari},
+  journal={Scientific Reports},
+  volume={12},
+  number={1},
+  pages={18734},
+  year={2022},
+  publisher={Nature Publishing Group UK London},
+  doi={10.1038/s41598-022-22832-7}
 }
 @techreport{lichtner2015pflotran,
   title={PFLOTRAN user manual: A massively parallel reactive flow and transport model for describing surface and subsurface processes},
   author={Lichtner, Peter C and Hammond, Glenn E and Lu, Chuan and Karra, Satish and Bisht, Gautam and Andre, Benjamin and Mills, Richard and Kumar, Jitendra},
   year={2015},
-  institution={Los Alamos National Lab.(LANL), Los Alamos, NM (United States); Sandia~…}
+  institution={Los Alamos National Lab.(LANL), Los Alamos, NM (United States); Sandia~…},
+  doi={10.2172/1168703}
 }
 @techreport{zyvoloski1997summary,
   title={Summary of the models and methods for the FEHM application-a finite-element heat-and mass-transfer code},
   author={Zyvoloski, George A and Robinson, Bruce A and Dash, Zora V and Trease, Lynn L},
   year={1997},
-  institution={Los Alamos National Lab.(LANL), Los Alamos, NM (United States)}
+  institution={Los Alamos National Lab.(LANL), Los Alamos, NM (United States)},
+  doi={10.2172/14903}
 }
 @book{harbaugh2005modflow,
   title={MODFLOW-2005, the US Geological Survey modular ground-water model: the ground-water flow process},
   author={Harbaugh, Arlen W},
   volume={6},
   year={2005},
-  publisher={US Department of the Interior, US Geological Survey Reston, VA, USA}
+  publisher={US Department of the Interior, US Geological Survey Reston, VA, USA},
+  doi={10.5066/F7RF5S7G}
 }

paper.md

@@ -60,7 +60,7 @@ An automatically-differentiable model like `DPFEHM` can be seamlessly integrated
 This enables machine learning workflows with `DPFEHM` in the loop, e.g., to learn to manage pressure in a scenario where wastewater or carbon dioxide are being injected into the subsurface [@pachalieva2022physics].
 For example, without automatic differentiation the machine learning would get stuck when it needed to compute a Jacobian-vector product involving the subsurface simulator.
 `DPFEHM` fills this gap and enables the efficient computation of the Jacobian-vector product.
-It is additionally useful for non-machine learning workflows, because gradient calculations are also ubiqitous in more traditional workflows such as inverse analysis [@wu2022inverse] and uncertainty quantification [@betancourt2017conceptual] (UQ).
+It is additionally useful for non-machine learning workflows, because gradient calculations are also ubiqitous in more traditional workflows such as inverse analysis [@wu2022inverse] and uncertainty quantification [@gelman2015stan] (UQ).
 For example, inverse analysis often uses the gradient to perform some variation of gradient descent to find the solution to the inverse problem, so making this fast is important in this context.
 Traditional inverse modeling and UQ tools (`PEST` [@doherty2010approaches] being the most widely used example), take a non-intrusive approach, which allows them to work with any simulator but forces them to treat the simulator as a black box.
 `DPFEHM` lays the groundwork for next-generation UQ tools that utilize the gradient and Jacobian information that `DPFEHM` efficiently provides.