initiated a new chaper for sensitivity analysis

vohramanav · dmcdougall · commit dd4ba59fb12c · 2016-11-18T18:40:41.000-06:00
diff --git a/manual/users/rawfigs/sensitivity_plot.pdf b/manual/users/rawfigs/sensitivity_plot.pdf
diff --git a/manual/users/sensitivity_mc.C b/manual/users/sensitivity_mc.C
@@ -0,0 +1,71 @@
+#include <cmath>
+#include <fstream>
+#include <iomanip> // for setprecision
+
+#include <queso/GslVector.h>
+#include <queso/GslMatrix.h>
+#include <sensitivity_mc.h>
+
+template<class P_V, class P_M, class Q_V, class Q_M>
+Qoi_mc<P_V, P_M, Q_V, Q_M>::Qoi_mc(const char * prefix,
+    const QUESO::VectorSet<P_V, P_M> & domainSet,
+    const QUESO::VectorSet<Q_V, Q_M> & imageSet)
+  : QUESO::BaseVectorFunction<P_V, P_M, Q_V, Q_M>(prefix, domainSet, imageSet),
+    x_loc(3)
+{
+}
+
+template<class P_V, class P_M, class Q_V, class Q_M>
+Qoi_mc<P_V, P_M, Q_V, Q_M>::~Qoi_mc()
+{
+  // Deconstruct here
+}
+
+template<class P_V, class P_M, class Q_V, class Q_M>
+void
+Qoi_mc<P_V, P_M, Q_V, Q_M>::compute(const P_V & domainVector,
+    const P_V * domainDirection,
+    Q_V & imageVector, QUESO::DistArray<P_V *> * gradVectors,
+    QUESO::DistArray<P_M *> * hessianMatrices,
+    QUESO::DistArray<P_V *> * hessianEffects) const
+{
+  if (domainVector.sizeLocal() != 2) {
+    queso_error_msg("domainVector does not have size 2");
+  }
+  if (imageVector.sizeLocal() != 1) {
+    queso_error_msg("imageVector does not have size 1");
+  }
+
+  qoi_samples.open ("c_qoi_samplesAi.txt", std::fstream::in | std::fstream::out | std::fstream::app);
+
+ // ----Generate Qoi using samples from a text files -------------
+ count++;
+ samples.open ("./files_sense/c_samples_Ai.txt", std::fstream::in | std::fstream::out | std::fstream::app);
+ 
+ int cou = 1;
+
+ while (samples >> mf >> cf){
+       if (cou == count){
+       		m = mf;
+ 		c = cf;
+       	break;
+       }
+       cou++;
+ }
+
+// ------- Generate Qoi using pseudo-random MC samples ------------
+//  std::cout << "m = " << domainVector[0] << std::endl; 
+//  m = domainVector[0];  // Sample of the RV 'line slope'
+//  c = domainVector[1];  // Sample of the RV 'y-intercept'
+  double y_obs = 0.0;
+  y_obs = m*x_loc + c;
+
+  qoi_samples << std::setprecision(4) << y_obs << "\t\t" << m << "\t\t" << c << std::endl;
+
+  imageVector[0] = y_obs;
+  qoi_samples.close();
+  samples.close();
+}
+
+template class Qoi_mc<QUESO::GslVector, QUESO::GslMatrix, QUESO::GslVector,
+                   QUESO::GslMatrix>;
diff --git a/manual/users/sensitivity_mc.h b/manual/users/sensitivity_mc.h
@@ -0,0 +1,30 @@
+#ifndef QUESO_EXAMPLE_SENSITIVITY_MC_H
+#define QUESO_EXAMPLE_SENSITIVITY_MC_H
+
+#include <queso/VectorFunction.h>
+#include <queso/DistArray.h>
+#include <fstream>
+
+template<class P_V = QUESO::GslVector, class P_M = QUESO::GslMatrix,
+	 class Q_V = QUESO::GslVector, class Q_M = QUESO::GslMatrix>
+class Qoi_mc : public QUESO::BaseVectorFunction<P_V, P_M, Q_V, Q_M>
+{
+public:
+	Qoi_mc(const char * prefix, const QUESO::VectorSet<P_V, P_M> & domainSet,
+	    const QUESO::VectorSet<Q_V, Q_M> & imageSet);
+	virtual ~Qoi_mc();
+	virtual void compute(const P_V & domainVector, const P_V * domainDirection,
+		Q_V & imageVector, QUESO::DistArray<P_V *> * gradVectors,
+		QUESO::DistArray<P_M *> * hessianMatrices,
+		QUESO::DistArray<P_V *> * hessianEffects) const;
+
+private:
+	double x_loc;
+	mutable double m,c,mf,cf,count;
+	mutable std::fstream qoi_samples;
+	mutable std::fstream samples;
+
+};
+
+#endif
+	
diff --git a/manual/users/uq.bib b/manual/users/uq.bib
@@ -1112,3 +1112,122 @@ @Misc{wiki:ablation
  OPTkeywords = {},
 }
 
+@article{Cukier:1973,
+  title={Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. {I} Theory},
+  author={Cukier, R.I. and Fortuin, C.M. and Shuler, K.E. and Petschek, A.G. and Schaibly, J.H.},
+  journal={The Journal of chemical physics},
+  volume={59},
+  number={8},
+  pages={3873--3878},
+  year={1973},
+  publisher={AIP Publishing}
+}
+
+@article{Sobol:1990,
+  title={On sensitivity estimation for nonlinear mathematical models},
+  author={Sobol', I.M.},
+  journal={Matematicheskoe Modelirovanie},
+  volume={2},
+  number={1},
+  pages={112--118},
+  year={1990},
+  publisher={Russian Academy of Sciences, Branch of Mathematical Sciences}
+}
+
+@book{Saltelli:2008,
+  title={Global sensitivity analysis: the primer},
+  author={Saltelli, A. and Ratto, M. and Andres, T. and Campolongo, F. and Cariboni, J. and Gatelli, D. and Saisana, M. and Tarantola, S.},
+  year={2008},
+  publisher={John Wiley \& Sons}
+}
+
+@article{Xiu:2002,
+  title={The Wiener--Askey polynomial chaos for stochastic differential equations},
+  author={Xiu, D. and Karniadakis, G.E.},
+  journal={SIAM journal on scientific computing},
+  volume={24},
+  number={2},
+  pages={619--644},
+  year={2002},
+  publisher={SIAM}
+}
+
+@book{Ghanem:2003,
+  title={Stochastic finite elements: a spectral approach},
+  author={Ghanem, R.G. and Spanos, P.D.},
+  year={2003},
+  publisher={Courier Corporation}
+}
+
+@article{Gutmann:2001,
+  title={A radial basis function method for global optimization},
+  author={Gutmann, H-M},
+  journal={Journal of Global Optimization},
+  volume={19},
+  number={3},
+  pages={201--227},
+  year={2001},
+  publisher={Springer}
+}
+
+@article{Saltelli:2010,
+  title={Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index},
+  author={Saltelli, A. and Annoni, P. and Azzini, I. and Campolongo, F. and Ratto, M. and Tarantola, S.},
+  journal={Computer Physics Communications},
+  volume={181},
+  number={2},
+  pages={259--270},
+  year={2010},
+  publisher={Elsevier}
+}
+
+@article{Jansen:1999,
+  title={Analysis of variance designs for model output},
+  author={Jansen, M.J.W.},
+  journal={Computer Physics Communications},
+  volume={117},
+  number={1},
+  pages={35--43},
+  year={1999},
+  publisher={Elsevier}
+}
+
+@article{Homma:1996,
+  title={Importance measures in global sensitivity analysis of nonlinear models},
+  author={Homma, T. and Saltelli, A.},
+  journal={Reliability Engineering \& System Safety},
+  volume={52},
+  number={1},
+  pages={1--17},
+  year={1996},
+  publisher={Elsevier}
+}
+
+@article{Sobol:2007,
+  title={Global sensitivity indices for the investigation of nonlinear mathematical models},
+  author={Sobol', I.M.},
+  journal={Matematicheskoe Modelirovanie},
+  volume={19},
+  number={11},
+  pages={23--24},
+  year={2007},
+  publisher={Russian Academy of Sciences, Branch of Mathematical Sciences}
+}
+
+@article{Vohra:2014,
+  title={Development of a reduced model of formation reactions in Zr-Al nanolaminates},
+  author={Vohra, M. and Winokur, J. and Overdeep, K.R. and Marcello, P. and Weihs, T.P. and Knio, O.M.},
+  journal={Journal of Applied Physics},
+  volume={116},
+  number={23},
+  pages={233501},
+  year={2014},
+  publisher={AIP Publishing}
+}
+
+@article{Vohra:2016,
+  title={Design Analysis for Optimal Calibration of Diffusivity in Reactive Multilayers},
+  author={Vohra, M. and Huan, X. and Weihs, T.P. and Knio, O.M.},
+  journal={arXiv preprint arXiv:1610.02558},
+  year={2016}
+}
diff --git a/manual/users/users.tex b/manual/users/users.tex
@@ -10,6 +10,12 @@
 \usepackage{rotating}
 \usepackage{color}
 \usepackage{url}
+\usepackage{bm}
+\usepackage{multirow}
+\usepackage{booktabs}
+\usepackage{algorithm}
+\usepackage{algpseudocode}
+\usepackage{amsmath,amssymb}
 \usepackage{fancyvrb}
 \usepackage{subfig}
 \usepackage{wrapfig}
@@ -20,8 +26,10 @@
 \usepackage{geometry}
 \geometry{top=1.00in, bottom=1.00in, left=1.00in, right=1.00in}
 \usepackage{enumerate}
-\usepackage[algoruled,vlined,linesnumbered,english]{algorithm2e}
+\usepackage[algoruled,vlined,linesnumbered,english,algo2e]{algorithm2e}
 \SetAlFnt{\footnotesize}
+\DeclareMathOperator{\E}{\mathbb{E}}
+\DeclareMathOperator{\V}{\mathbb{V}}
 
 \usepackage{titlesec}
 \titleformat{\section}{\LARGE\sffamily}{\thesection}{1em}{}
@@ -76,6 +84,8 @@
 \newcommand{\post}{\text{posterior}}
 \newcommand{\prior}{\text{prior}}
 \newcommand{\D}{ {\bf D}}
+\newcommand{\be}{\begin{equation}}
+\newcommand{\ee}{\end{equation}}
 
 \newcommand{\myverb}[1]{ \indent{ \begin{verbatim} #1 \end{verbatim} } }
 \newcommand{\QUESOversion}{0.51.0}
@@ -131,6 +141,10 @@
 %\newcommand{\new}[1]{\reversemarginpar{\color{red}\tiny\raggedright\textsf{\hspace{-30pt}\vspace{-25pt} NEW}} \begin{leftbar} #1 \end{leftbar}}
 \newcommand{\new}[1]{#1}
 
+\makeatletter
+\def\BState{\State\hskip-\ALG@thistlm}
+\makeatother
+
 \begin{document}
 
 \setlength{\unitlength}{1.0in}
@@ -155,6 +169,8 @@
 
 \include{users_4_remarks}
 
+\include{users_gsa}
+
 \include{users_5_examples}
 
 
diff --git a/manual/users/users_3_classes.tex b/manual/users/users_3_classes.tex
@@ -717,6 +717,7 @@ \subsection{Multilevel Solver (and Options)}\label{sec:ML}
 
 %\clearpage
 \subsection{Statistical Forward Problem (and Options)}
+\label{sub:SFP}
 
 A SFP in QUESO also has two input entities, the input (parameter) RV and a QoI function, and one output entity, the QoI RV. 
 The SIP is represented through the templated class \verb+StatisticalForwardProblem<P_V,P_M,Q_V,Q_M >+, which diagram is presented in Figure \ref{fig-sfp-class}. Again, the types \verb+P_V+ and \verb+Q_V+ of vectors and types \verb+P_M+ and \verb+Q_M+ of matrices, where \verb+P_+ stands for 'parameter' and \verb+Q_+ stands for 'quantities of interest'.
diff --git a/manual/users/users_5a_example_sip.tex b/manual/users/users_5a_example_sip.tex
@@ -133,11 +133,11 @@ \subsection{Example Code}\label{sec:sip-code}
 
 \lstinputlisting[caption=File \texttt{example\_likelihood.h}., label={fig-like-h}, linerange={25-1000}]{../../examples/simpleStatisticalInverseProblem/src/example_likelihood.h}
 
-\newpage
+%\newpage
 
 \lstinputlisting[caption=File \texttt{example\_likelihood.C}., label={fig-like-c}, linerange={25-1000}]{../../examples/simpleStatisticalInverseProblem/src/example_likelihood.C}
 
-\newpage
+%\newpage
 
 \lstinputlisting[caption=File \texttt{example\_compute.h.}, label={code:sip-compute-h}, linerange={25-1000}]{../../examples/simpleStatisticalInverseProblem/src/example_compute.h}
 
@@ -350,7 +350,7 @@ \subsubsection{Covariance and Correlation Matrices}
 
 Listing \ref{matlab:cov_matrix} presents the Matlab steps for calculating the covariance and correlation matrices for the parameters $\theta_1$ and $\theta_2$.
 
-\newpage
+%\newpage
 
 \begin{lstlisting}[label=matlab:ip_cov_matrix,caption={Matlab code for finding covariance and correlation matrices.}]
 % inside Matlab
diff --git a/manual/users/users_gsa.tex b/manual/users/users_gsa.tex
