more refs

paper.bib

@@ -65,3 +65,29 @@
   year={2008},
   school={Rice University}
 }
+
+@article{chen2017intelligent,
+  title={Intelligent adaptive sampling guided by Gaussian process inference},
+  author={Chen, Yuhang and Peng, Chaoyang},
+  journal={Measurement Science and Technology},
+  volume={28},
+  number={10},
+  pages={105005},
+  year={2017},
+  publisher={IOP Publishing}
+}
+
+@article{takhtaganov2018adaptive,
+  title={Adaptive Gaussian process surrogates for Bayesian inference},
+  author={Takhtaganov, Timur and M{\"u}ller, Juliane},
+  journal={arXiv preprint arXiv:1809.10784},
+  year={2018}
+}
+
+@online{mathematica_adaptive,
+  author = {Stephen Wolfram},
+  title = {{M}athematica: {A}daptive {P}lotting},
+  year = 2011,
+  url = {http://demonstrations.wolfram.com/AdaptivePlotting/},
+  urldate = {2019-09-10}
+}

paper.md

@@ -24,9 +24,9 @@ Even though it is suboptimal, one usually resorts to sampling $X$ on a homogeneo
 
 #### Choosing new points based on existing data improves the simulation efficiency.
 <!-- This should convey the point that it is advantageous to do this. -->
-A better alternative which improves the simulation efficiency is to choose new, potentially interesting points in $X$ based on existing data. [@gramacy2004parameter; @de1995adaptive; @castro2008active]<!-- cite i.e. hydrodynamics, Bayesian sampling -->
-Baysian optimization works well for high-cost simulations where one needs to find a minimum (or maximum).
-If the goal of the simulation is to approximate a contineous function with the least amount of points, the continuity of the approximation is achieved by a greedy algorithm that samples mid-points of intervals with the largest Euclidean distance. <!-- cite literature to support this claim that it is better, Mathematica and MATLAB maybe -->
+A better alternative which improves the simulation efficiency is to choose new, potentially interesting points in $X$ based on existing data. [@gramacy2004parameter; @de1995adaptive; @castro2008active; @chen2017intelligent] <!-- cite i.e. hydrodynamics-->
+Baysian optimization works well for high-cost simulations where one needs to find a minimum (or maximum). [@@takhtaganov2018adaptive]
+If the goal of the simulation is to approximate a contineous function with the least amount of points, the continuity of the approximation is achieved by a greedy algorithm that samples mid-points of intervals with the largest Euclidean distance. [@mathematica_adaptive] <!-- cite literature to support this claim that it is better, Mathematica and MATLAB maybe -->
 Such a sampling strategy would trivially speedup many simulations.
 One of the most significant complications here is to parallelize this algorithm, as it requires a lot of bookkeeping and planning ahead.