From 664d21362ab2ec5a971470c4728b65a09073ca35 Mon Sep 17 00:00:00 2001 From: Paul-Saves Date: Fri, 20 Dec 2024 17:23:23 +0100 Subject: [PATCH] update notebooks --- AUTHORS.md | 1 + tutorial/README.md | 42 +++++++++++++------------- tutorial/SBO/SMT_EGO_application.ipynb | 6 ++-- 3 files changed, 25 insertions(+), 24 deletions(-) diff --git a/AUTHORS.md b/AUTHORS.md index 6ad804e55..48e602624 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -19,6 +19,7 @@ SMT has been developed thanks to contributions from: * Ewout ter Hoeven * Florent Vergnes * Frederick Zahle +* Heine Røstum * Hugo Reimeringer * Hugo Valayer * Jasper Bussemaker diff --git a/tutorial/README.md b/tutorial/README.md index 87eb10c06..31742d27e 100644 --- a/tutorial/README.md +++ b/tutorial/README.md @@ -10,104 +10,104 @@ These tutorials introduce to use the opensource Surrogate Modeling Toolbox where ## Surrogate-based Optimization -### Efficient Global Optimization: How to start? +* ### Efficient Global Optimization: How to start? [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/SBO/SMT_SBO_EGO_Educational.ipynb) -### Bayesian Optimization - Efficient Global Optimization to solve unconstrained problems +* ### Bayesian Optimization - Efficient Global Optimization to solve expensive problems [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/SBO/SMT_EGO_application.ipynb) -### Bayesian Optimization with noisy data +* ### Bayesian Optimization with noisy data [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/SBO/SMT_EGO_noisyGP.ipynb) ## Multi-Fidelity Gaussian Process -### With required nested sampling +* ### With required nested sampling [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MultiFi/SMT_MFK_tutorial.ipynb) -#### With noise +* #### With noise [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MultiFi/SMT_MFK_Noise.ipynb) -#### Adaptative sampling +* #### Adaptative sampling [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MultiFi/ADOE_MFK_Forrester_1D2F.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MultiFi/ADOE_MFK_Rosenbrock_2D2F.ipynb) -### Without nested sampling +* ### Without nested sampling [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MultiFi/SMT_MFCK_tutorial.ipynb) ## Proper Orthogonal Decomposition and Interpolation -### PODI+I tutorial in SMT with global and local basis +* ### PODI+I tutorial in SMT with global and local basis [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/PODI/SMT_PODI_tutorial.ipynb) -### PODI+I application to airfoil design +* ### PODI+I application to airfoil design [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/PODI/SMT_PODI_Airfoil.ipynb) ## Kernel Engineering -### Kernel engineering tutorial in SMT +* ### Kernel engineering tutorial in SMT [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/Kernels/SMT_Kernel_tutorial.ipynb) -### Kernel engineering application to aeroelasticity prediction +* ### Kernel engineering application to aeroelasticity prediction [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/Kernels/SMT_Kernel_Hale.ipynb) ## Explainability and conformal prediction -### Warning: [The explainability usage tutorial has been moved to SMTorg/smt-explainability](https://github.com/SMTorg/smt-explainability) +* ### Warning: [The explainability usage tutorial has been moved to SMTorg/smt-explainability](https://github.com/SMTorg/smt-explainability) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt-explainability/blob/master/tutorial/Explainability_tools.ipynb) ## Other Gaussian Process Models and Sampling Methods -### LHS sampling (initial and expanded) +* ### LHS sampling (initial and expanded) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/Misc/SMT_ExpandedLHS.ipynb) -### Gaussian Process Trajectory Sampling +* ### Gaussian Process Trajectory Sampling [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/Misc/SMT_GP_Sampling.ipynb) -### Noisy Gaussian Process +* ### Noisy Gaussian Process [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/Misc/SMT_Noise.ipynb) -### Sparse Gaussian Process +* ### Sparse Gaussian Process [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/Misc/SMT_SGP_analytic.ipynb) -### Cooperative Components Kriging +* ### Cooperative Components Kriging [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/Misc/SMT_CoopCompKRG.ipynb) ## Mixed-integer and mixed-hierarchical surrogate models -### Warning: [The Design Space usage tutorial has been moved to SMTorg/smt-design-space-ext](https://github.com/SMTorg/smt-design-space-ext) +* ### Warning: [The Design Space usage tutorial has been moved to SMTorg/smt-design-space-ext](https://github.com/SMTorg/smt-design-space-ext) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt-design-space-ext/blob/master/tutorial/SMT_DesignSpace_example.ipynb) -### Specific notebook associated to the SMT 2.0 Journal Paper (submitted) with a focus on mixed integer and mixed hierarchical surrogate models (continuous, discrete, categorical) +* ### Specific notebook associated to the SMT 2.0 Journal Paper (submitted) with a focus on mixed integer and mixed hierarchical surrogate models (continuous, discrete, categorical) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MixedInteger/RunTestCases_Paper_SMT_v2.ipynb) -### Mixed-Integer Gaussian Process and Bayesian Optimization to solve unconstrained problems with mixed variables (continuous, discrete, categorical) +* ### Mixed-Integer Gaussian Process and Bayesian Optimization to solve unconstrained problems with mixed variables (continuous, discrete, categorical) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MixedInteger/SMT_MixedInteger.ipynb) -### Mixed-Integer Gaussian Process and Bayesian Optimization for Engineering application +* ### Mixed-Integer Gaussian Process and Bayesian Optimization for Engineering application [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/MixedInteger/SMT_MixedInteger_Engineering_applications.ipynb) diff --git a/tutorial/SBO/SMT_EGO_application.ipynb b/tutorial/SBO/SMT_EGO_application.ipynb index c29f293ec..d3859b0cb 100644 --- a/tutorial/SBO/SMT_EGO_application.ipynb +++ b/tutorial/SBO/SMT_EGO_application.ipynb @@ -16,7 +16,7 @@ "This tutorial describes how to use the SMT toolbox to do some Bayesian Optimization (EGO method) to solve unconstrained optimization problem\n", "
\n", " \n", - "Rémy Priem, Nathalie Bartoli, Paul Saves, Heine Røstum - December 2024\n", + "Rémy Priem, Nathalie Bartoli, Paul Saves, Heine Røstum, Joseph Morlier - December 2024\n", "\n", "based on `SMT 2.8.1 version` " ] @@ -1394,8 +1394,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finding the optimal variabel combination $\\textbf{x}^*$\n", - "The purpose is to find the optimal variabel combination $\\textbf{x}^*$, which can be found by maximizing the aquisition function $\\text{cEI(\\textbf{x})}$:\n", + "## Finding the optimal variable $\\textbf{x}^*$\n", + "The purpose is to find the optimal variable combination $\\textbf{x}^*$, which can be found by maximizing the aquisition function $\\text{cEI}(\\textbf{x})$:\n", "$$ \\textbf{x}^* = \\underset{\\textbf{x} \\in \\textbf{X}}{\\text{arg max }} \\text{cEI}(\\textbf{x})$$\n", "For the maximization purpose Evolutionary Strategy of the python package Pymoo is used." ]