diff --git a/calibration2024/calibration2024.bib b/calibration2024/calibration2024.bib index 986b6b6..13cfa92 100755 --- a/calibration2024/calibration2024.bib +++ b/calibration2024/calibration2024.bib @@ -1,19 +1,12 @@ @inproceedings{Baudin2021, - title={Linear algebra of linear and nonlinear bayesian calibration}, + title={Linear algebra of linear and nonlinear {Bayesian} calibration}, author={Baudin, Michaël and Lebrun, Régis}, year={2021}, booktitle = {UNCECOMP 2021}, pages = {339--353}, organization = "4th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering M. Papadrakakis, V. Papadopoulos, G. Stefanou (eds.) Streamed from Athens, Greece, 28-30 June 2021", } -@book{BinghamFry2010, - title={Regression, Linear Models in Statistics}, - author={N. H. Bingham and John M. Fry}, - series={Springer Undergraduate Mathematics Series}, - year={2010}, - publisher={Springer} -} @Book{Bjorck1996, author = {Ake Björck}, @@ -22,14 +15,6 @@ @Book{Bjorck1996 year = {1996}, } -@book{BinghamFry, - title={Regression, Linear Models in Statistics}, - author={N. H. Bingham and John M. Fry}, - series={Springer Undergraduate Mathematics Series}, - year={2010}, - publisher={Springer} -} - @INPROCEEDINGS{Hansen00thelcurve, author = {P. C. Hansen}, title = {The {L}-Curve and its Use in the Numerical Treatment of Inverse Problems}, @@ -64,20 +49,6 @@ @book{Evensen2009 publisher = {SIAM} } -@book{Sen1990, - author = "Ashish Sen and Muni Srivastava", - title = "Regression analysis", - year = "1990", - publisher = "Springer", -} - -@book{Draper1988, - author = "Norman R. Draper and Harry Smith", - title = "Applied Regression Analysis, Third Edition", - year = "1988", - publisher = "John Wiley \& Sons, Inc.", -} - @book{Tarantola2005, author = "Albert Tarantola", title = "Inverse problem theory", @@ -92,14 +63,6 @@ @book{Asch2016 publisher={SIAM} } -@incollection{iooss2015review, - title={A review on global sensitivity analysis methods}, - author={Iooss, Bertrand and Lema{\^\i}tre, Paul}, - booktitle={Uncertainty management in simulation-optimization of complex systems}, - pages={101--122}, - year={2015}, - publisher={Springer} -} @Inbook{Baudin2016, author="Baudin, Micha{\"e}l and Dutfoy, Anne and Iooss, Bertrand and Popelin, Anne-Laure", @@ -129,13 +92,6 @@ @techreport{BaudinMethodes2022 number={6125-3119-2022-00175-FR} } -@unpublished{OpenTURNSCalibrationFlooding, -author = "{Consortium OpenTURNS}", -title = "Calibration of the flooding model", -year = "2022", -note = "\url{http://openturns.github.io/openturns/master/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_flooding.html}", -} - @techreport{garbow1980implementation, title={Implementation guide for {MINPACK}-1.}, author={Garbow, Burton S. and Hillstrom, Kenneth E. and More, Jorge J.}, @@ -151,21 +107,23 @@ @book{kern2016methodes } -@book{hastie2009elements, - title={The elements of statistical learning: data mining, inference, and prediction}, - author={Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome H and Friedman, Jerome H}, - volume={2}, - year={2009}, - publisher={Springer} +@book{lawson1995solving, + title={Solving least squares problems}, + author={Lawson, Charles L. and Hanson, Richard J.}, + year={1995}, + publisher={SIAM} } -@article{Rocquigny2006LaMaitrise, - title={La maîtrise des incertitudes dans un contexte industriel. 1re partie: une approche méthodologique globale basée sur des exemples.}, - author={de Rocquigny, Etienne.}, - journal={Journal de la Société française de statistique}, - volume={147}, - number={3}, - pages={33-71}, - year={2006} +@book{idier2013bayesian, + title={Bayesian approach to inverse problems}, + author={Idier, J{\'e}r{\^o}me}, + year={2013}, + publisher={John Wiley \& Sons} } +@book{hansen2013least, + title={Least squares data fitting with applications}, + author={Hansen, Per Christian and Pereyra, Victor and Scherer, Godela}, + year={2013}, + publisher={JHU Press} +} diff --git a/calibration2024/calibration2024.tex b/calibration2024/calibration2024.tex index edcb993..4d7f945 100755 --- a/calibration2024/calibration2024.tex +++ b/calibration2024/calibration2024.tex @@ -59,28 +59,83 @@ \begin{frame} \frametitle{Introduction} -On dispose : +We have: \begin{itemize} -\item d'observations, -\item d'un modèle paramétrique. +\item a dataset, +\item a parametric model with unknown parameters. \end{itemize} -On cherche : +We search for: \begin{itemize} -\item des paramètres, -\item tels que les prédictions du modèle soient -\emph{proches} des observations. +\item parameter values, +\item such that the predictions of the model are as close as possible to the data. \end{itemize} +Since the dataset is random, we want the distribution of the parameters. + +From there, we can compute confidence intervals of the parameters. + \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{flooding_before_calibration.pdf} \end{center} -\caption{Des observations comparées à des prédictions.} +\caption{Observations compared to the predictions of a model.} \end{figure} \end{frame} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\begin{frame}[fragile] +\section{Overview} +\frametitle{Overview} + +In OpenTURNS, we have several calibration features: +\begin{itemize} +\item \href{https://openturns.github.io/openturns/latest/theory/data_analysis/data_analysis.html#calibration}{theory help pages} +\item \href{https://openturns.github.io/openturns/latest/user_manual/calibration.html}{API help pages} +\item \href{https://openturns.github.io/openturns/latest/auto_calibration/index.html}{examples}. +\end{itemize} + + +There are two types of features : +\begin{itemize} +\item linear and non linear least squares, Gaussian linear and non linear calibration : \pyvar{*Calibration} classes. These classes compute the \textbf{posterior distribution of the parameters}. +\item Monte Carlo Markov Chain (MCMC) algorithms : \pyvar{*MetropolisHastings}, etc. These classes \textbf{generate a sample from the posterior distribution of the parameters}. +\end{itemize} + +The simplest example is \href{https://openturns.github.io/openturns/latest/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_quickstart.html#sphx-glr-auto-calibration-least-squares-and-gaussian-calibration-plot-calibration-quickstart-py}{Calibrate a parametric model: a quick-start guide to calibration} + +Here, we are going to review the \href{https://openturns.github.io/openturns/latest/auto_calibration/least_squares_and_gaussian_calibration/plot_calibration_flooding.html#sphx-glr-auto-calibration-least-squares-and-gaussian-calibration-plot-calibration-flooding-py}{Calibration of the flooding model} +\end{frame} + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\begin{frame}[fragile] +\section{Conclusion} +\frametitle{Conclusion} + +Other tools : +\begin{itemize} +\item Calibration methods are also available in \href{https://persalys.fr}{Persalys} : linear and non linear least squares, Gaussian linear and non linear calibration. +\end{itemize} + +Perspectives: +\begin{itemize} +\item provide bounds to the optimization algorithms (return truncated normal distribution if necessary); +\item unify the \pyvar{ParametricFunction} in \pyvar{*Calibration} and \pyvar{*MetropolisHastings} classes (exchange the roles of $x$ and $\theta$); +\item calibrate parametric functions with field output more easily; +\item provide algorithms to automatically compute finite difference steps (not specific to calibration); +\item provide the covariance matrix of the parameters as a diagonal matrix; +\item scale the parameters to calibrate (not specific to calibration); +\item implement \pyvar{CalibrationResult.isBayesian()} (see \href{https://github.com/openturns/openturns/issues/2560}{2560}); +\item implement a `CalibrationResult` structure for M.-H. classes. +\end{itemize} +\end{frame} + + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{References} diff --git a/calibration2024/figures/MethodologieIncertitude-EN.pdf b/calibration2024/figures/MethodologieIncertitude-EN.pdf new file mode 100755 index 0000000..473f05f Binary files /dev/null and b/calibration2024/figures/MethodologieIncertitude-EN.pdf differ diff --git a/calibration2024/macros.tex b/calibration2024/macros.tex index 78be1b5..79a4d2b 100755 --- a/calibration2024/macros.tex +++ b/calibration2024/macros.tex @@ -18,6 +18,8 @@ \unitlength=1cm \graphicspath{{./figures/}} +\usepackage{hyperref} +\hypersetup{colorlinks=true, linkcolor=blue, linktocpage, urlcolor=blue} \def\bx{{\bf x}} \def\RR{\mathbb{R}}