{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import display, Latex\n", "from IPython.core.display import HTML\n", "css_file = './custom.css'\n", "HTML(open(css_file, \"r\").read()) " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version 3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]\n" ] } ], "source": [ "import sys #only needed to determine Python version number\n", "print('Python version ' + sys.version)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# M62 TP 4 - Étude de la convergence" ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

\n", "
" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(300000)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Autosaving every 300 seconds\n" ] } ], "source": [ "%reset -f\n", "%matplotlib inline\n", "%autosave 300\n", "\n", "from matplotlib.pylab import *\n", "# rcdefaults()\n", "rcParams.update({'font.size': 16})\n", "from scipy.optimize import fsolve" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WyG-bTRQE3f6" }, "source": [ "## Exercice\n", "\n", "Considérons le problème de Cauchy\n", "\n", ">trouver la fonction $y \\colon I\\subset \\mathbb{R} \\to \\mathbb{R}$ définie sur l'intervalle $I=[0,1]$ telle que\n", "$$\n", "\\begin{cases}\n", "y'(t) = y(t), &\\forall t \\in I=[0,1],\\\\\n", "y(0) = 1\n", "\\end{cases}\n", "$$\n", "dont la solution est $y(t)=e^{t}$. \n", "\n", "Estimer l'ordre de convergence des méthodes:\n", "1. EE, AB$_2$, AB$_3$, AB$_4$, AB$_5$, N$_2$, N$_3$, N$_4$, EM, RK4$_1$, RK6$_5$, RK7$_6$ \n", "2. EI, CN, AM$_2$, AM$_3$, AM$_4$, AM$_5$, MA$_2$, BDF$_2$, BDF$_3$, RK1_M\n", "3. Heun, AM$_4$-AB$_1$, AM$_4$-AB$_2$, AM$_4$-AB$_3$\n", " \n", " \n", "Remarque: puisque la fonction $\\varphi(t,y)=y$ est linéaire, toute méthode implicite peut être rendue explicite par un calcul élémentaire en explicitant directement pour chaque schéma l'expression de $u_{n+1}$. Cependant, nous pouvons utiliser le le module `SciPy` sans modifier l'implémentation des schémas (mais on payera l'ordre de convergence de `fsolve`).\n", "\n", "**Attention: les schémas multistep ont besoin d'initialiser plusieurs pas de la suite définie pas récurrence pour pouvoir démarrer. \n", "Dans cette étude, au lieu d'utiliser un schéma d'ordre inférieur pour initialiser la suite, on utilisera la solution exacte (en effet, l'utilisation d'un schéma d'ordre inférieur dégrade l'ordre de précision).**" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WyG-bTRQE3f6" }, "source": [ "
Rappels de la démarche pour le calcul de l'orde de convergence.\n", " \n", "+ On écrit les schémas numériques :\n", " + les nœuds d'intégration $[t_0,t_1,\\dots,t_{N}]$ sont contenus dans le vecteur `tt` (qui change en fonction de `h`) \n", " + les valeurs $[u_0,u_1,\\dots,u_{N}]$ pour chaque méthode sont contenues dans le vecteur `uu`.\n", "+ Pour chaque schéma, on calcule la solution approchée avec différentes valeurs de $h_k=1/N_k$. On sauvegarde les valeurs de $h_k$ dans le vecteur `H`. \n", "+ Pour chaque valeur de $h_k$, on calcule le maximum de la valeur absolue de l'erreur et on sauvegarde toutes ces erreurs dans le vecteur `err_schema` de sort que `err_schema[k]` contient $e_k=\\max_{i=0,\\dots,N_k}|y(t_i)-u_{i}|$. \n", "+ Pour afficher l'ordre de convergence on utilise une échelle logarithmique, i.e. on représente $\\ln(h)$ sur l'axe des abscisses et $\\ln(\\text{err})$ sur l'axe des ordonnées. \n", " En effet, si $\\text{err}=Ch^p$ alors $\\ln(\\text{err})=\\ln(C)+p\\ln(h)$. \n", " En échelle logarithmique, $p$ représente donc la pente de la ligne droite $\\ln(\\text{err})$.\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Schémas explicites" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "y69SGZjfIDo9" }, "source": [ "### Schéma de Adam-Bashforth à 1 pas = schéma d'Euler progressif\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{n+1}=u_n+h\\varphi(t_n,u_n)& n=0,1,2,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "_Bgo6mNyIQgu" }, "outputs": [], "source": [ "def EE(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(N):\n", " uu.append(uu[i]+h*phi(tt[i],uu[i]))\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bJ2pbhejIQM2" }, "source": [ "### Schéma de Adam-Bashforth à 2 pas\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{1}=y_1,\\\\\n", "u_{n+1}=u_n+\\frac{h}{2}\\Bigl(3\\varphi(t_n,u_n)-\\varphi(t_{n-1},u_{n-1})\\Bigr)& n=1,2,3,4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "g38fKrIgSiBQ" }, "outputs": [], "source": [ "def AB2(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(2)]\n", " for i in range(1,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " uu.append( uu[i] + (3*k1-k2)*h/2 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "nI8swyc6RxIR" }, "source": [ "### Schéma de Adam-Bashforth à 3 pas\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{1}=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{n+1}=u_n+\\frac{h}{12}\\Bigl(23\\varphi(t_n,u_n)-16\\varphi(t_{n-1},u_{n-1})+5\\varphi(t_{n-2},u_{n-2})\\Bigr)& n=2,3,4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "3ymFHJHrSkOh" }, "outputs": [], "source": [ "def AB3(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(3)]\n", " for i in range(2,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " uu.append( uu[i] + (23*k1-16*k2+5*k3)*h/12 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "awcWzBp7SXvQ" }, "source": [ "### Schéma de Adam-Bashforth à 4 pas\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{1}=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{3}=y_3,\\\\\n", "u_{n+1}=u_n+\\frac{h}{24}\\Bigl(55\\varphi(t_n,u_n)-59\\varphi(t_{n-1},u_{n-1})+37\\varphi(t_{n-2},u_{n-2})-9\\varphi(t_{n-3},u_{n-3})\\Bigr)& n=3,4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "-r1BaNeLTrHq" }, "outputs": [], "source": [ "def AB4(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0] \n", " uu = [sol_exacte(tt[i]) for i in range(4)]\n", " for i in range(3,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " k4 = phi( tt[i-3], uu[i-3] )\n", " uu.append( uu[i] + (55*k1-59*k2+37*k3-9*k4)*h/24 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "heLmvMe_S0y6" }, "source": [ "### Schéma de Adam-Bashforth à 5 pas\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{1}=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{3}=y_3,\\\\\n", "u_{4}=y_4,\\\\\n", "u_{n+1}=u_n+\\frac{h}{720}\\Bigl(1901\\varphi(t_n,u_n)-2774\\varphi(t_{n-1},u_{n-1})+2616\\varphi(t_{n-2},u_{n-2})-1274\\varphi(t_{n-3},u_{n-3})+251\\varphi(t_{n-4},u_{n-4})\\Bigr)& n=4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "yPXMx8CITt4C" }, "outputs": [], "source": [ "def AB5(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(5)]\n", " for i in range(4,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " k4 = phi( tt[i-3], uu[i-3] )\n", " k5 = phi( tt[i-4], uu[i-4] )\n", " uu.append( uu[i] + (1901*k1-2774*k2+2616*k3-1274*k4+251*k5)*h/720 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OldEmxFfTJfq" }, "source": [ "### Schéma de Nylström à 2 pas\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{1}=y_1,\\\\\n", "u_{n+1}=u_{n-1}+2h\\varphi(t_{n},u_{n})& n=1,2,3,4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "BCR9Z7VzTxEN" }, "outputs": [], "source": [ "def N2(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " uu.append(sol_exacte(tt[1]))\n", " for i in range(1,N):\n", " k1 = phi( tt[i], uu[i] )\n", " uu.append( uu[i-1] + 2*h*k1 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dADQEhyYTVQz" }, "source": [ "### Schéma de Nylström à 3 pas\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{1}=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{n+1}=u_{n-1}+\\frac{h}{3}\\Bigl(7\\varphi(t_{n},u_{n})-2\\varphi(t_{n-1},u_{n-1})+\\varphi(t_{n-2},u_{n-2})\\Bigr)& n=2,3,4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "bS1FABgRTzdC" }, "outputs": [], "source": [ "def N3(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(3)]\n", " for i in range(2,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " uu.append( uu[i-1] + (7*k1-2*k2+k3)*h/3 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LsgdqQnfTf66" }, "source": [ "### Schéma de Nylström à 4 pas\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{1}=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{3}=y_3,\\\\\n", "u_{n+1}=u_{n-1}+\\frac{h}{3}\\Bigl(8\\varphi(t_{n},u_{n})-5\\varphi(t_{n-1},u_{n-1})+4\\varphi(t_{n-2},u_{n-2})-\\varphi(t_{n-3},u_{n-3})\\Bigr)& n=3,4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "dbDTaW5LUcss" }, "outputs": [], "source": [ "def N4(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(4)]\n", " for i in range(3,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " k4 = phi( tt[i-3], uu[i-3] )\n", " uu.append( uu[i-1] + (8*k1-5*k2+4*k3-k4)*h/3 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "p4f0txAsIwNG" }, "source": [ "### Schéma d'Euler modifié\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "\\tilde u = u_n+\\frac{h}{2}\\varphi(t_n,u_n),\\\\\n", "u_{n+1}=u_n+h\\varphi\\left(t_n+\\frac{h}{2},\\tilde u\\right)& n=0,1,2,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "O5rOYvtPI7TO" }, "outputs": [], "source": [ "def EM(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(N):\n", " k1 = h * phi( tt[i], uu[i] )\n", " uu.append( uu[i]+h*phi(tt[i]+h/2,uu[i]+k1/2) )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_50Xo95mT9tC" }, "source": [ "### Schéma de Runge-Kutta RK4-1\n", "$$\\begin{cases}\n", "u_0 = y_0 \\\\\n", "K_1 = \\varphi\\left(t_n,u_n\\right)\\\\\n", "K_2 = \\varphi\\left(t_n+\\frac{h}{2},u_n+\\frac{h}{2} K_1)\\right)\\\\\n", "K_3 = \\varphi\\left(t_n+\\frac{h}{2},u_n+\\frac{h}{2}K_2\\right)\\\\\n", "K_4 = \\varphi\\left(t_{n+1},u_n+h K_3\\right)\\\\\n", "u_{n+1} = u_n + \\frac{h}{6}\\left(K_1+2K_2+2K_3+K_4\\right) & n=0,1,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "rbRn1INwGY-8" }, "outputs": [], "source": [ "def RK4(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i]+h/2 , uu[i]+h*k1/2 )\n", " k3 = phi( tt[i]+h/2 , uu[i]+h*k2/2 )\n", " k4 = phi( tt[i+1] , uu[i]+h*k3 )\n", " uu.append( uu[i] + (k1+2*k2+2*k3+k4)*h/6 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_50Xo95mT9tC" }, "source": [ "### Schéma de Runge-Kutta RK6_5 (Butcher à 6 étages d'ordre 5)\n", "$$\\begin{array}{c|cccccc} 0 & 0 & 0 & 0 & 0 &0&0 \\\\ \\frac{1}{4} & \\frac{1}{4} & 0&0&0&0&0\\\\ \\frac{1}{4} & \\frac{1}{8} &\\frac{1}{8}&0&0&0&0\\\\ \\frac{1}{2} & 0 &-\\frac{1}{2}&1&0&0&0\\\\ \\frac{3}{4} & \\frac{3}{16} &0&0&\\frac{9}{16}&0&0\\\\ 1 & \\frac{-3}{7} &\\frac{2}{7}&\\frac{12}{7}&\\frac{-12}{7}&\\frac{8}{7}&0\\\\ \\hline & \\frac{7}{90} & 0&\\frac{32}{90} & \\frac{12}{90} & \\frac{32}{90} & \\frac{7}{90} \\end{array}$$\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "rbRn1INwGY-8" }, "outputs": [], "source": [ "def RK6_5(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(N):\n", " k1 = phi( tt[i] , uu[i] )\n", " k2 = phi( tt[i]+h/4 , uu[i]+h*k1/4 )\n", " k3 = phi( tt[i]+h/4 , uu[i]+h*(k1+k2)/8 )\n", " k4 = phi( tt[i]+h/2 , uu[i]+h*(-k2+2*k3)/2 )\n", " k5 = phi( tt[i]+h*3/4, uu[i]+h*(3*k1+9*k4)/16 )\n", " k6 = phi( tt[i+1] , uu[i]+h*(-3*k1+2*k2+12*k3-12*k4+8*k5)/7 )\n", " uu.append( uu[i] + (7*k1+32*k3+12*k4+32*k5+7*k6)*h/90 )\n", " return uu\n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_50Xo95mT9tC" }, "source": [ "### Schéma de Runge-Kutta RK7_6 (Butcher à 7 étages d'ordre 6)\n", "$$\n", "\\begin{array}{c|ccccccc} \n", "0 & 0 & 0 & 0 & 0 &0&0&0 \\\\ \n", "\\frac{1}{2} & \\frac{1}{2} & 0&0&0&0&0&0\\\\ \n", "\\frac{2}{3} & \\frac{2}{9} &\\frac{4}{9}&0&0&0&0&0\\\\ \n", "\\frac{1}{3} & \\frac{7}{36} &\\frac{2}{9}&-\\frac{1}{12}&0&0&0&0\\\\ \n", "\\frac{5}{6} & -\\frac{35}{144} &-\\frac{55}{36}&\\frac{35}{48}&\\frac{15}{8}&0&0&0\\\\ \n", "\\frac{1}{6} & -\\frac{1}{360} &-\\frac{11}{36}&-\\frac{1}{8}&\\frac{1}{2}&\\frac{1}{10}&0&0\\\\ \n", "1 & \\frac{-41}{260} &\\frac{22}{13}&\\frac{43}{156}&-\\frac{118}{39}&\\frac{32}{195}&\\frac{80}{39}&0\\\\ \n", "\\hline \n", " & \\frac{13}{200} & 0&\\frac{11}{40} & \\frac{11}{40} & \\frac{4}{25} & \\frac{4}{25} & \\frac{13}{200} \\end{array}$$\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "rbRn1INwGY-8" }, "outputs": [], "source": [ "def RK7_6(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(N):\n", " k1 = phi( tt[i] , uu[i] )\n", " k2 = phi( tt[i]+h/2 , uu[i]+h*( k1 )/2 )\n", " k3 = phi( tt[i]+h*2/3, uu[i]+h*( 2*k1 +4*k2 )/9 )\n", " k4 = phi( tt[i]+h/3 , uu[i]+h*( 7*k1 +8*k2 -3*k3 )/36 )\n", " k5 = phi( tt[i]+h*5/6, uu[i]+h*( -35*k1 -220*k2 +105*k3 +270*k4 )/144 )\n", " k6 = phi( tt[i]+h/6 , uu[i]+h*( -k1 -110*k2 -45*k3 +180*k4 +36*k5 )/360 )\n", " k7 = phi( tt[i+1] , uu[i]+h*(-41/260*k1 +22/13*k2 +43/156*k3 -118/39*k4 +32/195*k5 +80/39*k6) )\n", " uu.append( uu[i] + (13/200*k1+11/40*k3+11/40*k4+4/25*k5+4/25*k6+13/200*k7)*h )\n", " return uu " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Schémas implicites" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "y69SGZjfIDo9" }, "source": [ "### Schéma d'Euler régressif\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{n+1}=u_n+h\\varphi(t_{n+1},u_{n+1})& n=0,1,2,\\dots N-1\n", "\\end{cases}\n", "$$\n", "avec $u_{n+1}$ zéro de la fonction $$x\\mapsto -x+u_n+h\\varphi(t_{n+1},x).$$" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "_Bgo6mNyIQgu" }, "outputs": [], "source": [ "def EI(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(N):\n", " temp = fsolve(lambda x: -x+uu[i]+h*phi(tt[i+1],x), uu[i])\n", " uu.append(temp[0])\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma de Crank-Nicolson\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{n+1}=u_n+\\frac{h}{2}\\Bigl(\\varphi(t_n,u_n)+\\varphi(t_{n+1},u_{n+1})\\Bigr)& n=0,1,2,\\dots N-1\n", "\\end{cases}\n", "$$\n", "avec $u_{n+1}$ un zéro de la fonction $x\\mapsto -x+u_n+\\frac{h}{2}(\\varphi(t_n,u_n)+\\varphi(t_{n+1},x))$." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def CN(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(len(tt)-1):\n", " temp = fsolve(lambda x: -x+uu[i]+0.5*h*( phi(tt[i+1],x)+phi(tt[i],uu[i]) ), uu[i])\n", " uu.append(temp[0])\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma de AM-2\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_1=y_1,\\\\\n", "u_{n+1}=u_n+\\frac{h}{12}\\Bigl(5\\varphi(t_{n+1},u_{n+1})+8\\varphi(t_n,u_n)-\\varphi(t_{n-1},u_{n-1})\\Bigr)& n=1,2,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def AM2(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(2)]\n", " for i in range(1,N):\n", " temp = fsolve(lambda x: -x+uu[i]+h*( 5*phi(tt[i+1],x)+8*phi(tt[i],uu[i])-phi(tt[i-1],uu[i-1]) )/12, uu[i])\n", " uu.append(temp[0])\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma de AM-3\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_1=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{n+1}=u_n+\\frac{h}{24}\\Bigl(9\\varphi(t_{n+1},u_{n+1})+19\\varphi(t_n,u_n)-5\\varphi(t_{n-1},u_{n-1})+\\varphi(t_{n-2},u_{n-2})\\Bigr)& n=2,3,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def AM3(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(3)]\n", " for i in range(2,N):\n", " temp = fsolve(lambda x: -x+uu[i]+h*( 9*phi(tt[i+1],x)+19*phi(tt[i],uu[i])-5*phi(tt[i-1],uu[i-1])+phi(tt[i-2],uu[i-2]) )/24, uu[i])[0]\n", " uu.append(temp)\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma de AM-4\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_1=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{3}=y_3,\\\\\n", "u_{n+1}=u_n+\\frac{h}{720}\\Bigl(251\\varphi(t_{n+1},u_{n+1})+646\\varphi(t_n,u_n)-264\\varphi(t_{n-1},u_{n-1})+106\\varphi(t_{n-2},u_{n-2})-19\\varphi(t_{n-3},u_{n-3})\\Bigr)& n=3,4,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def AM4(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(4)]\n", " for i in range(3,N):\n", " temp = fsolve(lambda x: -x+uu[i]+h*( 251*phi(tt[i+1],x)+646*phi(tt[i],uu[i])-264*phi(tt[i-1],uu[i-1])+106*phi(tt[i-2],uu[i-2])-19*phi(tt[i-3],uu[i-3]) )/720, uu[i])[0]\n", " uu.append(temp)\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma de AM-5\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_1=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{3}=y_3,\\\\\n", "u_{4}=y_4,\\\\\n", "u_{n+1}=u_n+\\frac{h}{1440}\\Bigl(475\\varphi(t_{n+1},u_{+1})+1427\\varphi(t_n,u_n)-798\\varphi(t_{n-1},u_{n-1})+482\\varphi(t_{n-2},u_{n-2})-173\\varphi(t_{n-3},u_{n-3})+27\\varphi(t_{n-4},u_{n-4})\\Bigr),& n=4,5,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def AM5(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(5)]\n", " for i in range(4,N):\n", " temp = fsolve(lambda x: -x+uu[i]+h*( 475*phi(tt[i+1],x)+1427*phi(tt[i],uu[i])-798*phi(tt[i-1],uu[i-1])+482*phi(tt[i-2],uu[i-2])-173*phi(tt[i-3],uu[i-3])+27*phi(tt[i-4],uu[i-4]))/1440, uu[i])[0]\n", " uu.append(temp)\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma MS-2\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_1=y_1,\\\\\n", "u_{n+1}=u_{n-1}+\\frac{h}{3}\\Bigl(\\varphi(t_{n+1},u_{n+1})+4\\varphi(t_n,u_n)+\\varphi(t_{n-1},u_{n-1})\\Bigr)& n=1,2,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def MS2(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(2)]\n", " for i in range(1,N):\n", " temp = fsolve(lambda x: -x+uu[i-1]+h*(phi(tt[i+1],x)+4*phi(tt[i],uu[i])+phi(tt[i-1],uu[i-1]) )/3, uu[i])[0]\n", " uu.append(temp)\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma BDF2\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_1=y_1,\\\\\n", "u_{n+1}=\\frac{4}{3}u_n-\\frac{1}{3}u_{n-1}+\\frac{2}{3}h\\varphi(t_{n+1},u_{n+1})& n=1,2,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def BDF2(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(2)]\n", " for i in range(1,N):\n", " temp = fsolve(lambda x: -x+4/3*uu[i]-1/3*uu[i-1] + 2/3*h*phi(tt[i+1],x) , uu[i])[0]\n", " uu.append(temp)\n", " return uu " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma BDF3\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_1=y_1,\\\\\n", "u_{2}=y_2,\\\\\n", "u_{n+1}=\\frac{18}{11}u_n-\\frac{9}{11}u_{n-1}+\\frac{2}{11}u_{n-2}+\\frac{6}{11}h\\varphi(t_{n+1},u_{n+1})& n=2,3,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def BDF3(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(3)]\n", " for i in range(2,N):\n", " temp = fsolve(lambda x: -x+18/11*uu[i]-9/11*uu[i-1] + 2/11*uu[i-2]+6/11*h*phi(tt[i+1],x) , uu[i])[0]\n", " uu.append(temp)\n", " return uu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Schéma RK1_M:\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "u_{n+1}=u_n+h\\varphi\\left(\\frac{t_n+t_{n+1}}{2},\\frac{u_n+u_{n+1}}{2}\\right)& n=0,1,2,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def RK1_M(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(len(tt)-1):\n", " uu.append( fsolve(lambda x: -x+uu[i]+h*phi( (tt[i]+tt[i+1])/2,(uu[i]+x)/2 ), uu[i])[0] )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Schémas predicteur-correcteur" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma de Heun\n", "$$\n", "\\begin{cases}\n", "u_0=y_0,\\\\\n", "\\tilde u = u_n+h\\varphi(t_n,u_n)\\\\\n", "u_{n+1}=u_n+\\frac{h}{2}\\Bigl(\\varphi(t_n,u_n)+\\varphi(t_{n+1},\\tilde u)\\Bigr)& n=0,1,2,\\dots N-1\n", "\\end{cases}\n", "$$" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "1ewZyxhHRYxg" }, "outputs": [], "source": [ "def heun(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " for i in range(N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i+1], uu[i] + k1*h )\n", " uu.append( uu[i] + (k1+k2)*h/2 )\n", " return uu" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "23PyYGzhQwuo" }, "source": [ "### Schéma AM-4 AB-2/3/4/5\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "def AM4AB2(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(4)]\n", " for i in range(3,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " pred = uu[i] + (3*k1-k2)*h/2\n", " uu.append(uu[i]+h*( 251*phi(tt[i+1],pred)+646*phi(tt[i],uu[i])-264*phi(tt[i-1],uu[i-1])+106*phi(tt[i-2],uu[i-2])-19*phi(tt[i-3],uu[i-3]) )/720)\n", " return uu\n", " \n", "def AM4AB3(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[0])]\n", " uu.append(sol_exacte(tt[1]))\n", " uu.append(sol_exacte(tt[2]))\n", " uu.append(sol_exacte(tt[3]))\n", " for i in range(3,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " pred = uu[i] + (23*k1-16*k2+5*k3)*h/12\n", " uu.append(uu[i]+h*( 251*phi(tt[i+1],pred)+646*phi(tt[i],uu[i])-264*phi(tt[i-1],uu[i-1])+106*phi(tt[i-2],uu[i-2])-19*phi(tt[i-3],uu[i-3]) )/720)\n", " return uu\n", " \n", "def AM4AB4(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(4)]\n", " for i in range(3,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " k4 = phi( tt[i-3], uu[i-3] )\n", " pred = uu[i] + (55*k1-59*k2+37*k3-9*k4)*h/24\n", " uu.append(uu[i]+h*( 251*phi(tt[i+1],pred)+646*phi(tt[i],uu[i])-264*phi(tt[i-1],uu[i-1])+106*phi(tt[i-2],uu[i-2])-19*phi(tt[i-3],uu[i-3]) )/720)\n", " return uu\n", " \n", "def AM4AB5(phi,tt,sol_exacte):\n", " h = tt[1]-tt[0]\n", " uu = [sol_exacte(tt[i]) for i in range(5)] \n", " for i in range(4,N):\n", " k1 = phi( tt[i], uu[i] )\n", " k2 = phi( tt[i-1], uu[i-1] )\n", " k3 = phi( tt[i-2], uu[i-2] )\n", " k4 = phi( tt[i-3], uu[i-3] )\n", " k5 = phi( tt[i-4], uu[i-4] )\n", " pred = uu[i] + (1901*k1-2774*k2+2616*k3-1274*k4+251*k5)*h/720\n", " uu.append(uu[i]+h*( 251*phi(tt[i+1],pred)+646*phi(tt[i],uu[i])-264*phi(tt[i-1],uu[i-1])+106*phi(tt[i-2],uu[i-2])-19*phi(tt[i-3],uu[i-3]) )/720)\n", " return uu\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convergence" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cnwNf75iGe0F" }, "source": [ "On initialise le problème de Cauchy, on définit l'équation différentielle (`phi` est une fonction python qui contient la fonction mathématique $\\varphi(t, y)$ dépendant des variables $t$ et $y$) et enfin on définit la solution exacte:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "OLLu4aFJFENg" }, "outputs": [], "source": [ "t0, y0, tfinal = 0, 1, 3\n", "\n", "def sol_exacte(t):\n", " return exp(t)\n", " #return exp(-t)\n", " #return sqrt(2.*t+1.)\n", " #return sqrt(t**2+1.)\n", " #return 1./sqrt(1.-2.*t)\n", " \n", "def phi(t,y):\n", " return y\n", " #return -y\n", " #return 1./y \n", " #return t/y \n", " #return y**3 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour chaque schéma, on calcule la solution approchée avec différentes valeurs de $h_k=1/N_k$; on sauvegarde les valeurs de $h_k$ dans le vecteur `H`. \n", "\n", "Pour chaque valeur de $h_k$, on calcule le maximum de la valeur absolue de l'erreur et on sauvegarde toutes ces erreurs dans le vecteur `err_schema` de sort que `err_schema[k]` contient $e_k=\\max_{i=0,...,N_k}|y(t_i)-u_{i}|$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons utiliser deux méthodes différentes pour stocker ces informations. \n", "\n", "### Méthode 1\n", "\n", "La première méthode est celle utilisée lors des deux premiers TP: on crée autant de liste que de solutions approchée." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "H = []\n", "\n", "err_ep = []\n", "err_AB2 = []\n", "err_AB3 = []\n", "err_AB4 = []\n", "err_AB5 = []\n", "err_N2 = []\n", "err_N3 = []\n", "err_N4 = []\n", "err_RK4 = []\n", "err_RK6_5 = []\n", "err_RK7_6 = []\n", "\n", "err_er = []\n", "err_CN = []\n", "err_AM2 = []\n", "err_AM3 = []\n", "err_AM4 = []\n", "err_AM5 = []\n", "err_BDF2 = []\n", "err_BDF3 = []\n", "err_MS2=[]\n", "\n", "err_em = []\n", "err_heun = []\n", "err_AM4AB2 = []\n", "err_AM4AB3 = []\n", "err_AM4AB4 = []\n", "err_AM4AB5 = []\n", "\n", "N = 10\n", "for k in range(6):\n", " N += 50 #2**(k+3)\n", " tt = linspace(t0,tfinal,N+1)\n", " h = tt[1]-tt[0]\n", " H.append(h)\n", " yy = array([sol_exacte(t) for t in tt])\n", " \n", " # schemas explicites\n", " uu_ep = EE(phi,tt,sol_exacte)\n", " uu_AB2 = AB2(phi,tt,sol_exacte)\n", " uu_AB3 = AB3(phi,tt,sol_exacte)\n", " uu_AB4 = AB4(phi,tt,sol_exacte)\n", " uu_AB5 = AB5(phi,tt,sol_exacte)\n", " uu_N2 = N2(phi,tt,sol_exacte)\n", " uu_N3 = N3(phi,tt,sol_exacte)\n", " uu_N4 = N4(phi,tt,sol_exacte)\n", " uu_em = EM(phi,tt,sol_exacte)\n", " uu_RK4 = RK4(phi,tt,sol_exacte)\n", " uu_RK6_5 = RK6_5(phi,tt,sol_exacte)\n", " uu_RK7_6 = RK7_6(phi,tt,sol_exacte)\n", " # schemas implicites\n", " uu_er = EI(phi,tt,sol_exacte)\n", " uu_CN = CN(phi,tt,sol_exacte)\n", " uu_AM2 = AM2(phi,tt,sol_exacte)\n", " uu_AM3 = AM3(phi,tt,sol_exacte)\n", " uu_AM4 = AM4(phi,tt,sol_exacte)\n", " uu_AM5 = AM5(phi,tt,sol_exacte)\n", " uu_BDF2 = BDF2(phi,tt,sol_exacte)\n", " uu_BDF3 = BDF3(phi,tt,sol_exacte)\n", " uu_MS2 = MS2(phi,tt,sol_exacte)\n", " # schemas predictor-corrector\n", " uu_heun = heun(phi,tt,sol_exacte)\n", " uu_AM4AB2 = AM4AB2(phi,tt,sol_exacte)\n", " uu_AM4AB3 = AM4AB3(phi,tt,sol_exacte)\n", " uu_AM4AB4 = AM4AB4(phi,tt,sol_exacte)\n", " uu_AM4AB5 = AM4AB5(phi,tt,sol_exacte)\n", " \n", " # erreurs\n", " err_ep.append( norm(uu_ep-yy,inf))\n", " err_AB2.append(norm(uu_AB2-yy,inf))\n", " err_AB3.append(norm(uu_AB3-yy,inf))\n", " err_AB4.append(norm(uu_AB4-yy,inf))\n", " err_AB5.append(norm(uu_AB5-yy,inf))\n", " err_N2.append( norm(uu_N2-yy,inf))\n", " err_N3.append(norm(uu_N3-yy,inf))\n", " err_N4.append(norm(uu_N4-yy,inf))\n", " err_em.append(norm(uu_em-yy,inf))\n", " err_RK4.append(norm(uu_RK4-yy,inf))\n", " err_RK6_5.append(norm(uu_RK6_5-yy,inf))\n", " err_RK7_6.append(norm(uu_RK7_6-yy,inf))\n", " \n", " err_er.append(norm(uu_er-yy,inf))\n", " err_CN.append(norm(uu_CN-yy,inf))\n", " err_AM2.append(norm(uu_AM2-yy,inf))\n", " err_AM3.append(norm(uu_AM3-yy,inf))\n", " err_AM4.append(norm(uu_AM4-yy,inf))\n", " err_AM5.append(norm(uu_AM5-yy,inf))\n", " err_BDF2.append(norm(uu_BDF2-yy,inf))\n", " err_BDF3.append(norm(uu_BDF3-yy,inf))\n", " err_MS2.append(norm(uu_MS2-yy,inf))\n", " \n", " err_heun.append(norm(uu_heun-yy,inf))\n", " err_AM4AB2.append(norm(uu_AM4AB2-yy,inf))\n", " err_AM4AB3.append(norm(uu_AM4AB3-yy,inf))\n", " err_AM4AB4.append(norm(uu_AM4AB4-yy,inf))\n", " err_AM4AB5.append(norm(uu_AM4AB5-yy,inf))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "SnKKU27oGyQb" }, "source": [ "Pour estimer l'ordre de convergence on estime la pente de la droite qui relie l'erreur au pas $k$ à l'erreur au pas $k+1$ en echelle logarithmique en utilisant la fonction `polyfit` basée sur la régression linéaire. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "ySox-VsNGt8p" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EE 0.97\n", "AB2 1.98\n", "AB3 2.96\n", "AB4 3.94\n", "AB5 4.92\n", "N2 1.99\n", "N3 2.97\n", "N4 3.95\n", "EM 1.98\n", "RK4 3.98\n", "RK6_5 4.95\n", "RK7_6 6.18\n", "\n", "\n", "EI 1.04\n", "CN 2.00\n", "AM2 2.98\n", "AM3 3.96\n", "AM4 4.95\n", "AM5 5.93\n", "BDF2 1.97\n", "BDF3 2.95\n", "MS2 4.00\n", "\n", "\n", "Heun 1.98\n", "AM4_AB2 2.95\n", "AM4_AB3 3.94\n", "AM4_AB4 4.93\n", "AM4_AB5 4.76\n" ] } ], "source": [ "# print (f'EE {polyfit(log(H),log(err_ep), 1)[0]:1.2f}' )\n", "# print (f'AB2 {polyfit(log(H),log(err_AB2), 1)[0]:1.2f}' )\n", "# print (f'AB3 {polyfit(log(H),log(err_AB3), 1)[0]:1.2f}' )\n", "# print (f'AB4 {polyfit(log(H),log(err_AB4), 1)[0]:1.2f}' )\n", "# print (f'AB5 {polyfit(log(H),log(err_AB5), 1)[0]:1.2f}' )\n", "# print (f'N2 {polyfit(log(H),log(err_N2), 1)[0]:1.2f}' )\n", "# print (f'N3 {polyfit(log(H),log(err_N3), 1)[0]:1.2f}' )\n", "# print (f'N4 {polyfit(log(H),log(err_N4), 1)[0]:1.2f}' )\n", "# print (f'EM {polyfit(log(H),log(err_em), 1)[0]:1.2f}' )\n", "# print (f'RK4 {polyfit(log(H),log(err_RK4), 1)[0]:1.2f}' )\n", "# print (f'RK6_5 {polyfit(log(H),log(err_RK6_5), 1)[0]:1.2f}' )\n", "# print (f'RK7_6 {polyfit(log(H),log(err_RK7_6), 1)[0]:1.2f}' )\n", "# print('\\n')\n", "# print (f'EI {polyfit(log(H),log(err_er), 1)[0]:1.2f}' )\n", "# print (f'CN {polyfit(log(H),log(err_CN), 1)[0]:1.2f}' )\n", "# print (f'AM2 {polyfit(log(H),log(err_AM2), 1)[0]:1.2f}' )\n", "# print (f'AM3 {polyfit(log(H),log(err_AM3), 1)[0]:1.2f}' )\n", "# print (f'AM4 {polyfit(log(H),log(err_AM4), 1)[0]:1.2f}' )\n", "# print (f'AM5 {polyfit(log(H),log(err_AM5), 1)[0]:1.2f}' )\n", "# print (f'BDF2 {polyfit(log(H),log(err_BDF2), 1)[0]:1.2f}' ) \n", "# print (f'BDF3 {polyfit(log(H),log(err_BDF3), 1)[0]:1.2f}' )\n", "# print (f'MS2 {polyfit(log(H),log(err_MS2), 1)[0]:1.2f}' )\n", "# print('\\n')\n", "# print (f'Heun {polyfit(log(H),log(err_heun), 1)[0]:1.2f}' )\n", "# print (f'AM4_AB2 {polyfit(log(H),log(err_AM4AB2), 1)[0]:1.2f}' )\n", "# print (f'AM4_AB3 {polyfit(log(H),log(err_AM4AB3), 1)[0]:1.2f}' )\n", "# print (f'AM4_AB4 {polyfit(log(H),log(err_AM4AB4), 1)[0]:1.2f}' )\n", "# print (f'AM4_AB5 {polyfit(log(H),log(err_AM4AB5), 1)[0]:1.2f}' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Si on note\n", "- $\\omega_{[C]}$ l'ordre du corrector (schéma implicite) \n", "- $\\omega_{[P]}$ l'ordre du predictor (schéma explicite) \n", "alors l'ordre du schéma predictor-corrector est\n", "$$\\omega_{[PC]}=\\min\\{\\omega_{[C]},\\omega_{[P]}+1\\}$$\n", "\n", "**Pour les schémas AM4-ABx, le schéma corrector est d'ordre $p=5$, pour ne pas perdre en ordre convergence il faut choisir un schéma predictor d'ordre $p-1$ (ici AB4).**" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4HPS2hE6G54k" }, "source": [ "Pour afficher l'ordre de convergence on utilise une échelle logarithmique : on représente $\\ln(h)$ sur l'axe des abscisses et $\\ln(\\text{err})$ sur l'axe des ordonnées. Le but de cette représentation est clair: si $\\text{err}=Ch^p$ alors $\\ln(\\text{err})=\\ln(C)+p\\ln(h)$. En échelle logarithmique, $p$ représente donc la pente de la ligne droite $\\ln(\\text{err})$." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "base_uri": "https://localhost:8080/", "height": 3818, "output_extras": [ { "item_id": 1 }, { "item_id": 2 }, { "item_id": 3 }, { "item_id": 4 }, { "item_id": 5 }, { "item_id": 6 }, { "item_id": 7 }, { "item_id": 8 }, { "item_id": 9 }, { "item_id": 10 }, { "item_id": 11 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 2188, "status": "ok", "timestamp": 1520423878951, "user": { "displayName": "Gloria Faccanoni", "photoUrl": "//lh4.googleusercontent.com/-gY6sCpFtBJo/AAAAAAAAAAI/AAAAAAAABdo/a_W4-RMG5X0/s50-c-k-no/photo.jpg", "userId": "116371262733782746288" }, "user_tz": -60 }, "id": "oz1tVYNtG4-3", "outputId": "9b89f5ec-83c5-4797-e057-8d6c20560051" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure(figsize=(24,7))\n", "\n", "subplot(1,3,1)\n", "loglog(H,err_ep, 'r-o',label=f'AB-1 = EE {polyfit(log(H),log(err_ep), 1)[0]:1.2f}')\n", "loglog(H,err_AB2, 'g-+',label=f'AB2 {polyfit(log(H),log(err_AB2), 1)[0]:1.2f}')\n", "loglog(H,err_AB3, 'c-D',label=f'AB3 {polyfit(log(H),log(err_AB3), 1)[0]:1.2f}')\n", "loglog(H,err_AB4, 'y-*',label=f'AB4 {polyfit(log(H),log(err_AB4), 1)[0]:1.2f}')\n", "loglog(H,err_AB5, 'r-.',label=f'AB5 {polyfit(log(H),log(err_AB5), 1)[0]:1.2f}')\n", "loglog(H,err_N2, 'y-*',label=f'N2 {polyfit(log(H),log(err_N2), 1)[0]:1.2f}')\n", "loglog(H,err_N3, 'r-<',label=f'N3 {polyfit(log(H),log(err_N3), 1)[0]:1.2f}')\n", "loglog(H,err_N4, 'b-+',label=f'N4 {polyfit(log(H),log(err_N4), 1)[0]:1.2f}')\n", "loglog(H,err_em, 'c-o',label=f'EM {polyfit(log(H),log(err_em), 1)[0]:1.2f}')\n", "loglog(H,err_RK4, 'g-o',label=f'RK4_1 {polyfit(log(H),log(err_RK4), 1)[0]:1.2f}')\n", "loglog(H,err_RK6_5, 'b-*',label=f'RK6_5 {polyfit(log(H),log(err_RK6_5), 1)[0]:1.2f}')\n", "loglog(H,err_RK7_6, 'r-D',label=f'RK7_6 {polyfit(log(H),log(err_RK7_6), 1)[0]:1.2f}')\n", "xlabel('$h$')\n", "ylabel('$e$')\n", "title(\"Schemas explicites\")\n", "legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=1)\n", "grid(True)\n", "\n", "subplot(1,3,2)\n", "loglog(H,err_er, 'r-o',label=f'AM-0 = EI {polyfit(log(H),log(err_er), 1)[0]:1.2f}')\n", "loglog(H,err_CN, 'b-v',label=f'AM-1 = CN {polyfit(log(H),log(err_CN), 1)[0]:1.2f}')\n", "loglog(H,err_AM2, 'm->',label=f'AM2 {polyfit(log(H),log(err_AM2), 1)[0]:1.2f}')\n", "loglog(H,err_AM3, 'c-D',label=f'AM3 {polyfit(log(H),log(err_AM3), 1)[0]:1.2f}')\n", "loglog(H,err_AM4, 'g-+',label=f'AM4 {polyfit(log(H),log(err_AM4), 1)[0]:1.2f}')\n", "loglog(H,err_AM5, 'b->',label=f'AM5 {polyfit(log(H),log(err_AM5), 1)[0]:1.2f}')\n", "loglog(H,err_BDF2,'y-*',label=f'BDF2 {polyfit(log(H),log(err_BDF2), 1)[0]:1.2f}')\n", "loglog(H,err_BDF3,'r-<',label=f'BDF3 {polyfit(log(H),log(err_BDF3), 1)[0]:1.2f}')\n", "loglog(H,err_MS2, 'm-o',label=f'MS2 {polyfit(log(H),log(err_MS2), 1)[0]:1.2f}')\n", "xlabel('$h$')\n", "ylabel('$e$')\n", "title(\"Schemas implicites\")\n", "legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=1)\n", "grid(True)\n", "\n", "subplot(1,3,3)\n", "loglog(H,err_heun, 'y->',label=f'Heun {polyfit(log(H),log(err_heun), 1)[0]:1.2f}')\n", "loglog(H,err_AM4AB2, 'r-<',label=f'AM4_AB2 {polyfit(log(H),log(err_AM4AB2), 1)[0]:1.2f}')\n", "loglog(H,err_AM4AB3, 'b-v',label=f'AM4_AB3 {polyfit(log(H),log(err_AM4AB3), 1)[0]:1.2f}' )\n", "loglog(H,err_AM4AB4, 'm-*',label=f'AM4_AB4 {polyfit(log(H),log(err_AM4AB4), 1)[0]:1.2f}')\n", "loglog(H,err_AM4AB5, 'g-+',label=f'AM4_AB5 {polyfit(log(H),log(err_AM4AB5), 1)[0]:1.2f}' )\n", "xlabel('$h$')\n", "ylabel('$e$')\n", "title(\"Schemas predicteur-correcteur\")\n", "legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=1)\n", "grid(True)\n", "\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Version compacte\n", "\n", "De manière compacte en utilisant une liste des noms des schémas et des dictionnaires:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "schemasEXPL = ['EE','AB2','AB3','AB4','AB5','N2','N3','N4', 'EM', 'RK4', 'RK6_5', 'RK7_6']\n", "schemasIMPL = ['EI', 'CN', 'AM2', 'AM3', 'AM4', 'AM5', 'BDF2', 'BDF3', 'MS2', 'RK1_M']\n", "schemasPRCO = ['heun', 'AM4AB2', 'AM4AB3', 'AM4AB4', 'AM4AB5']\n", "\n", "schemas = schemasEXPL+schemasIMPL+schemasPRCO\n", "\n", "H = []\n", "err = { schemas[s] : [] for s in range(len(schemas)) }\n", "\n", "N=10\n", "for k in range(6):\n", " N += 50\n", " tt = linspace(t0,tfinal,N+1)\n", " h = tt[1]-tt[0]\n", " H.append(h)\n", " yy = array([sol_exacte(t) for t in tt])\n", " uu = { s : eval(s)(phi,tt,sol_exacte) for s in schemas }\n", " for key in uu:\n", " err[key].append(norm(uu[key]-yy,inf))\n", "\n", "ordre = { key : polyfit(log(H),log(err[key]),1)[0] for key in err }\n", "\n", " \n", "figure(figsize=(24,7))\n", "markers=['^', 's', 'p', 'h', '8', 'D', '>', '<', '*','+','o', 'x']\n", "\n", "subplot(1,3,1)\n", "for idx,s in enumerate(schemasEXPL):\n", " loglog(H,err[s], label=f'{s}: {ordre[s]:1.2f}',marker=markers[idx])\n", "xlabel('$h$')\n", "ylabel('$e$')\n", "title(\"Schemas explicites\")\n", "legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=1)\n", "grid(True)\n", "\n", "subplot(1,3,2)\n", "for idx,s in enumerate(schemasIMPL):\n", " loglog(H,err[s], label=f'{s}: {ordre[s]:1.2f}',marker=markers[idx])\n", "xlabel('$h$')\n", "ylabel('$e$')\n", "title(\"Schemas implicites\")\n", "legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=1)\n", "grid(True)\n", "\n", "subplot(1,3,3)\n", "for idx,s in enumerate(schemasPRCO):\n", " loglog(H,err[s], label=f'{s}: {ordre[s]:1.2f}',marker=markers[idx])\n", "xlabel('$h$')\n", "ylabel('$e$')\n", "title(\"Schemas predicteur-correcteur\")\n", "legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, ncol=1)\n", "grid(True);\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "default_view": {}, "name": "EdoExplicites.ipynb", "provenance": [], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": true, "user_envs_cfg": true }, "toc": { "base_numbering": 1, "nav_menu": { "height": "190.994px", "width": "160px" }, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "vscode": { "interpreter": { "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" } } }, "nbformat": 4, "nbformat_minor": 4 }