update notebooks

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

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)

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",
     "<div>\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."
    ]