From b4e9ea70c351c9e85c927f2869f2577a976ab3be Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Tue, 10 Sep 2019 12:19:51 +0200
Subject: [PATCH] more refs
---
paper.bib | 26 ++++++++++++++++++++++++++
paper.md | 6 +++---
2 files changed, 29 insertions(+), 3 deletions(-)
diff --git a/paper.bib b/paper.bib
index 20fd186..72f0fc2 100755
--- a/paper.bib
+++ b/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}
+}
diff --git a/paper.md b/paper.md
index e0c85e5..3926860 100755
--- a/paper.md
+++ b/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.
--
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