advection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-26 19:02:30.714936: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from modules.models import AdvectionPinn, create_dense_model\n",
    "from modules.data import simulate_advection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = 20.0\n",
    "k = 2.0\n",
    "def f_u(x):\n",
    "    r = v / k\n",
    "    return (1 - tf.exp(r * x)) / (1 - tf.exp(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-26 18:53:09.185431: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.234724: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.235124: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.236400: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-12-26 18:53:09.237181: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.237575: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.237827: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.969997: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.970374: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.970731: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-12-26 18:53:09.970931: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3026 MB memory:  -> device: 0, name: NVIDIA GeForce MX150, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    }
   ],
   "source": [
    "(x_eqn, y_eqn), (x_bnd, u_bnd) = simulate_advection(n_samples=1000)\n",
    "u_exact = f_u(x_eqn)\n",
    "inputs = [x_eqn, x_bnd]\n",
    "outputs = [u_exact, y_eqn, u_bnd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone = create_dense_model([64]*9, tf.nn.elu, tf.keras.initializers.HeNormal(seed=10), 1, 1)\n",
    "pinn = AdvectionPinn(backbone, v=v, k=k, loss_residual_weight=1.0, loss_boundary_weight=150.0)\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.001, decay_steps=500, decay_rate=0.9)\n",
    "pinn.compile(optimizer=tf.keras.optimizers.Adam(lr_schedule))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = pinn.fit(inputs, outputs, epochs=20000, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from modules.plots import plot_training_loss_linlog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_loss_linlog(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from modules.plots import plot_advection_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 183ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hm7XHx8cjKyurx32nTJkCb29vzJkzB/v37+/0eWNjIwICAuDr64uf/OQnyMnJGZDzEhERkeXaeX0ixc+mjoZCLvqTaYNGtG9WXV0Ng8EAT0/z5Ozp6YnKysou9/H29samTZuQnp6O3bt3IywsDHPmzMHBgwdNfcaOHYutW7fi888/x44dO6BUKjFjxgzk5+f3+7wA0NraCp1OZ7YRERGRZbiia8H+i1UAgEQrviULAKIvySyRmE9BFgShU9sNYWFhCAsLM/0cGxuL0tJSvPnmm5g1axYAICYmBjExMaY+M2bMwNSpU/HOO+/g7bff7td5ASA5ORmrV6/u/RcjIiKiYWNXdhkMRgFRAc64y2OU2OUMKtFG7tzc3CCTyTqNllVVVXUaVetJTEyMaVSuK1KpFNOmTTP16e95V6xYAa1Wa9pKS0t7XSMRERGJx2AUsPNEx6RJax+1A0QMdwqFApGRkcjMzDRrz8zMRFxcXK+Pk5OTA29v724/FwQBubm5pj79Pa+trS1UKpXZRkRERMPfN5eqUFrbDLWdDX4y0UfscgadqLdlk5KSsGjRIkRFRSE2NhabNm1CSUkJlixZAqBjtKy8vBypqakAgA0bNiAwMBDh4eHQ6/X4+OOPkZ6ejvT0dNMxV69ejZiYGISEhECn0+Htt99Gbm4u3n333V6fl4iIiKxH6tFiAMCCKF/YKWS36W35RA13iYmJqKmpwZo1a6DRaBAREYG9e/ciICAAAKDRaMzWntPr9Vi2bBnKy8thZ2eH8PBw7NmzB/PmzTP1qa+vx69+9StUVlZCrVZjypQpOHjwIKZPn97r8xIREZF1KK65hm8uXQUAPBEzMn7PSwRBEMQuwhLpdDqo1WpotVreoiUiIhqmXtt7HpsOFuLuUHd8+Mvpt99hGOtt9rDeRV6IiIhoRGtpM5heN7Y4dmSM2gEMd0RERGSlPj9VgfqmNvg62+GeMA+xyxkyDHdERERkdQRBwEfXJ1I8ERMAmbT7tWytDcMdERERWZ2c0nqcKddCIZdiQZT1r213M4Y7IiIisjpbDhUBAB6e5AMXB4XI1QwthjsiIiKyKqW1TfjirAYA8OzMMSJXM/QY7oiIiMiqfHDkMowCMDPEDWFe1v0e2a4w3BEREZHV0Da3Ie36e2T/ZwSO2gEMd0RERGRF0k6U4JregFBPR8wMcRO7HFEw3BEREZFVaDMYsfXIZQDAsz8aA4lk5Cx/cjOGOyIiIrIKe89oUKFtgZujAg9N9hG7HNEw3BEREZHFEwQB//imEACwKCYQShuZyBWJh+GOiIiILN7+i1U4r9HBQSEbUe+R7QrDHREREVk0QRDw96+/B9DxqjHnEbZo8a0Y7oiIiMiiHS2swXcl9VDIpXhmZpDY5YiO4Y6IiIgs2o1Ru4XT/OAxSilyNeJjuCMiIiKL9V1JHbIKaiCXSvDc3cFilzMsMNwRERGRxXr3+qjdT6eOxmgnO5GrGR4Y7oiIiMginSnT4r8XqiCVAL++5y6xyxk2GO6IiIjIIv018yIA4OHJoxHk5iByNcMHwx0RERFZnJOXa3Hg4lXIpBIsnRsidjnDCsMdERERWRRBEPDGVx2jdguifBHgylG7mzHcERERkUU5/H01jhXVQiGT4rezOWp3K4Y7IiIishiCIODNjEsAgF/E+MOHM2Q7YbgjIiIii7H3TCVOldbDzkaG5zlDtkuih7uUlBQEBQVBqVQiMjIShw4d6rbvgQMHIJFIOm0XLlww9dm8eTNmzpwJZ2dnODs7Y+7cuTh+/LjZcV599dVOx/Dy8hq070hERER3rrXdgP/35XkAwK9mjYH7KFuRKxqeRA13aWlpWLp0KV5++WXk5ORg5syZSEhIQElJSY/7Xbx4ERqNxrSFhPxwv/3AgQN47LHHsH//fhw9ehT+/v6Ij49HeXm52THCw8PNjnHmzJlB+Y5EREQ0MD46WozS2mZ4jLLFc3ePEbucYUsu5snXr1+PZ555Bs8++ywAYMOGDfjqq6+wceNGJCcnd7ufh4cHnJycuvxs27ZtZj9v3rwZu3btwn//+18sXrzY1C6XyzlaR0REZCHqrunx9n/zAQDL4sNgrxA1wgxroo3c6fV6ZGdnIz4+3qw9Pj4eWVlZPe47ZcoUeHt7Y86cOdi/f3+PfZuamtDW1gYXFxez9vz8fPj4+CAoKAgLFy5EYWFhj8dpbW2FTqcz24iIiGhovP11PnQt7RjrNQo/i/QVu5xhTbRwV11dDYPBAE9PT7N2T09PVFZWdrmPt7c3Nm3ahPT0dOzevRthYWGYM2cODh482O15li9fjtGjR2Pu3LmmtujoaKSmpuKrr77C5s2bUVlZibi4ONTU1HR7nOTkZKjVatPm5+fXx29MRERE/fF9VQM+OloMAHj5gXGQSSUiVzS8iT6mKZGYXyBBEDq13RAWFoawsDDTz7GxsSgtLcWbb76JWbNmdeq/bt067NixAwcOHIBSqTS1JyQkmP59woQJiI2NRXBwMD788EMkJSV1ee4VK1aYfabT6RjwiIiIBpkgCFj52Tm0GwXMHeeBmSHuYpc07Ik2cufm5gaZTNZplK6qqqrTaF5PYmJikJ+f36n9zTffxGuvvYaMjAxMnDixx2M4ODhgwoQJXR7nBltbW6hUKrONiIiIBte/T2twtLAGtnIpVj0YLnY5FkG0cKdQKBAZGYnMzEyz9szMTMTFxfX6ODk5OfD29jZre+ONN/DnP/8ZX375JaKiom57jNbWVpw/f77TcYiIiEg8ja3tWPufPADAb+69C34u9iJXZBlEvS2blJSERYsWISoqCrGxsdi0aRNKSkqwZMkSAB23QsvLy5GamgqgYzZtYGAgwsPDodfr8fHHHyM9PR3p6emmY65btw4rV67E9u3bERgYaBoZdHR0hKOjIwBg2bJlePDBB+Hv74+qqiqsXbsWOp0OTz755BD/L0BERETd2ZB5CVUNrQh0tcevZnHpk94SNdwlJiaipqYGa9asgUajQUREBPbu3YuAgAAAgEajMVvzTq/XY9myZSgvL4ednR3Cw8OxZ88ezJs3z9QnJSUFer0ejz76qNm5Vq1ahVdffRUAUFZWhsceewzV1dVwd3dHTEwMvv32W9N5iYiISFxnyrT4IOsyAODVh8KhtJGJW5AFkQiCIIhdhCXS6XRQq9XQarV8/o6IiGgA6duNeOjvh3GhsgE/meiNvz8+VeyShoXeZg/RXz9GREREdLOUA9/jQmUDXBwUWP0QJ1H0FcMdERERDRvnNTr8/evvAQCrHwqHqyPfH9tXDHdEREQ0LLQZjPjDrtNoNwqIH++Jn0zkKhb9wXBHREREw8I7/83HmXIt1HY2WDs/otuXGlDPGO6IiIhIdNnFdfj7/o7bsX95JAIeKuVt9qDuMNwRERGRqK61tiPpk1wYBWD+ZB/8ZKKP2CVZNIY7IiIiEtXaPXkormmCj1qJ1Q9HiF2OxWO4IyIiItHsy7uCHcdLAQBvLpgEtZ2NyBVZPoY7IiIiEkV1YyuW7z4NAHj2R0GIC3YTuSLrwHBHREREQ04QBCxPP4PqRj3CPEdh2X1hYpdkNRjuiIiIaMjtOF6KfeevQCGT4q3EyXx37ABiuCMiIqIhdbGyAav/fQ4AsOy+UIz34TvaBxLDHREREQ2ZZr0Bv93xHVrbjZgV6o5nfzRG7JKsDsMdERERDZk1/8nDpSuNcB9li/ULJkEq5VsoBhrDHREREQ2J/5yuwI7jJZBIgLcWTIabo63YJVklhjsiIiIadKW1TViRfgYA8Pw9wfhRCJc9GSwMd0RERDSo2gxG/HZHDhpa2xEZ4Iylc0PFLsmqMdwRERHRoHoz4yJyS+uhUsrxt4WTYSNj/BhM8v7sJAgCvvnmGxw6dAiXL19GU1MT3N3dMWXKFMydOxd+fn4DXScRERFZoIxzlXjvm0IAwOs/mwhfZ3uRK7J+fYrOzc3NeO211+Dn54eEhATs2bMH9fX1kMlk+P7777Fq1SoEBQVh3rx5+PbbbwerZiIiIrIAl6uv4f/75ykAwC9nBCFhgrfIFY0MfRq5Cw0NRXR0NP7xj3/gvvvug41N55f7FhcXY/v27UhMTMQrr7yC//mf/xmwYomIiMgytLQZ8Ott36GhpeM5uxXzxopd0oghEQRB6G3ns2fPIiIiold99Xo9iouLERIS0u/ihjOdTge1Wg2tVguViitrExER3SAIAl7adRq7ssvg6qDAnt/NhJdaKXZZFq+32aNPt2V7G+wAQKFQWG2wIyIiou6lnSjFruwySCXAO49NYbAbYv2errJy5UoYDIZO7VqtFo899tgdFUVERESW6UyZFn/6/MZ7Y8MQdxfXsxtq/Q53qampmDFjBgoKCkxtBw4cwIQJE3D58uWBqI2IiIgsSN01PX69LRv6diPmjvPEklnBYpc0IvU73J0+fRqBgYGYPHkyNm/ejJdeegnx8fF46qmncPjw4V4fJyUlBUFBQVAqlYiMjMShQ4e67XvgwAFIJJJO24ULF8z6paenY/z48bC1tcX48ePx6aef3tF5iYiIqGftBiNe2PEdyuqa4e9ij7/yvbGi6Xe4U6vV2LlzJ373u9/hueeew9/+9jd88cUXWLNmDWQyWa+OkZaWhqVLl+Lll19GTk4OZs6ciYSEBJSUlPS438WLF6HRaEzbzc/2HT16FImJiVi0aBFOnTqFRYsWYcGCBTh27Ngdn5eIiIi6tnbPeRz5vgb2Chk2LY6E2q7ziho0NPo0W/ZW77zzDv74xz/ikUceQXZ2NmQyGbZv345Jkyb1av/o6GhMnToVGzduNLWNGzcO8+fPR3Jycqf+Bw4cwL333ou6ujo4OTl1eczExETodDp88cUXprb7778fzs7O2LFjR7/O2xXOliUiIurwyYlS/CH9NADgH09E4v4IL5Ersk6DMlv2ZgkJCVi9ejVSU1Oxbds25OTkYNasWYiJicG6detuu79er0d2djbi4+PN2uPj45GVldXjvlOmTIG3tzfmzJmD/fv3m3129OjRTse87777TMfs73lbW1uh0+nMNiIiopEuu7gWL392BgDw+7mhDHbDQL/DXXt7O06fPo1HH30UAGBnZ4eNGzdi165deOutt267f3V1NQwGAzw9Pc3aPT09UVlZ2eU+3t7e2LRpE9LT07F7926EhYVhzpw5OHjwoKlPZWVlj8fsz3kBIDk5GWq12rTxFWtERDTSVdQ347mPvkObQUBChBd+O/susUsi9PPdsgCQmZnZZfsDDzyAM2fO9Po4Eon5w5aCIHRquyEsLAxhYWGmn2NjY1FaWoo333wTs2bN6tMx+3JeAFixYgWSkpJMP+t0OgY8IiIasVraDHjuo2xUN7ZirNcovPlzTqAYLvo9ctcTN7eONW16epzPzc0NMpms02hZVVVVp1G1nsTExCA/P9/0s5eXV4/H7O95bW1toVKpzDYiIqKRyGgUsOyfp3CmXAtnextsXhwFB9t+jxfRAOtTuBs3bhy2b98OvV7fY7/8/Hz8+te/xuuvv95tH4VCgcjIyE4jgJmZmYiLi+t1TTk5OfD2/uFFxLGxsZ2OmZGRYTrmQJ2XiIhopPpr5kX857QGcqkEKb+IhJ+Lvdgl0U36FLPfffdd/PGPf8RvfvMbxMfHIyoqCj4+PlAqlairq0NeXh4OHz6MvLw8vPDCC3j++ed7PF5SUhIWLVqEqKgoxMbGYtOmTSgpKcGSJUsAdNwKLS8vR2pqKgBgw4YNCAwMRHh4OPR6PT7++GOkp6cjPT3ddMwXX3wRs2bNwuuvv46HH34Y//rXv7Bv3z6ztfdud14iIiLq2icnSvHu/o4XGCT/dAJig11Frohu1adwN3v2bJw4cQJZWVlIS0vD9u3bcfnyZTQ3N8PNzQ1TpkzB4sWL8cQTT3S7VMnNEhMTUVNTgzVr1kCj0SAiIgJ79+5FQEAAAECj0ZitPafX67Fs2TKUl5fDzs4O4eHh2LNnD+bNm2fqExcXh507d+KVV17BypUrERwcjLS0NERHR/f6vERERNTZke+r8b+fdjxX/9vZd+HnUXz2fDi6o3XuRjKuc0dERCNJ/pUG/HRjFhpa2vHwZB9sSJzc40REGni9zR79fvpxzZo1PX7+pz/9qb+HJiIiomHkakMrnvrgBBpa2jEt0BnrHp3IYDeM9Tvc3fq+1ra2NhQVFUEulyM4OJjhjoiIyAo0tLTh6a3HUV7fjCA3B2xaFAVbee9eM0ri6He4y8nJ6dSm0+nw1FNP4ZFHHrmjooiIiEh8re0da9mdLdfB1UGB95+aBmcHhdhl0W0M6Dp3KpUKa9aswcqVKwfysERERDTEDEYBSWmnkFVQAweFDFufno4gNwexy6JeGPBFjOvr66HVagf6sERERDREBEHA6n+fw54zGtjIJHhvURQm+KrFLot6qd+3Zd9++22znwVBgEajwUcffYT777//jgsjIiIicbzz9fdIPVoMiQR4K3EyfhTiJnZJ1Af9DndvvfWW2c9SqRTu7u548sknsWLFijsujIiIiIbetmPFWJ95CQDw6oPh+MlEH5Eror7qd7grKioayDqIiIhIZP/KLccrn50F0LFI8ZNxgeIWRP0y4M/cERERkeX58qwGSZ+cgiAAv4j2R9KPQ8UuifqJ4Y6IiGiE23+hCr/dkQODUcCjkb7488MRXKTYgjHcERERjWBHvq/Gcx9no80g4CcTvfH6zyZCKmWws2QMd0RERCPU8aJaPPvhSejbjYgf74m3EidDxmBn8RjuiIiIRqATl2vx9AfH0dxmwN2h7njn8SmwkTEWWIN+z5YlIiIiy/RtYQ1+ufUEmvQGxAW74r1FkXxfrBVhuCMiIhpBjnxfjWc+PIGWNiNmhrhh8+IoKG0Y7KwJwx0REdEIcfDSVfxP6km0thtxT5g7/vFEJIOdFWK4IyIiGgH2X6zCcx9lQ99uxJyxHkh5YipvxVophjsiIiIr9+9TFUj6JBdtBgHx4z3x98enQiHn5AlrxXBHRERkxT7+thgr/3UWggD8ZKI33kqczFmxVo7hjoiIyAoJgoB393+PNzMuAeh4pdiahyO4jt0IwHBHRERkZYxGAX/Zex5bDhcBAH47+y4k/TiUrxQbIRjuiIiIrEi7wYg/pp9B+ndlAICVPxmPZ34UJHJVNJQY7oiIiKxEQ0sbfrM9BwcvXYVMKsG6n03EzyJ9xS6LhhjDHRERkRXQaJvx9AcncKGyAXY2Mrzz2BTMHe8pdlkkAtGny6SkpCAoKAhKpRKRkZE4dOhQr/Y7cuQI5HI5Jk+ebNZ+zz33QCKRdNoeeOABU59XX3210+deXl4D+bWIiIiGzLkKLea/ewQXKhvg5miLtOdiGOxGMFFH7tLS0rB06VKkpKRgxowZeO+995CQkIC8vDz4+/t3u59Wq8XixYsxZ84cXLlyxeyz3bt3Q6/Xm36uqanBpEmT8POf/9ysX3h4OPbt22f6WSbjQo5ERGR59l+swgvbvsM1vQEhHo744Olp8HW2F7ssEpGoI3fr16/HM888g2effRbjxo3Dhg0b4Ofnh40bN/a433PPPYfHH38csbGxnT5zcXGBl5eXacvMzIS9vX2ncCeXy836ubu7D+h3IyIiGmwff1uMZz88iWt6A+KCXbHr13EMdiReuNPr9cjOzkZ8fLxZe3x8PLKysrrd74MPPkBBQQFWrVrVq/Ns2bIFCxcuhIODg1l7fn4+fHx8EBQUhIULF6KwsLDH47S2tkKn05ltREREYtC3G/G/n57BK5+dhcEo4GdTfbH16elQ29mIXRoNA6KFu+rqahgMBnh6mj8T4OnpicrKyi73yc/Px/Lly7Ft2zbI5be/o3z8+HGcPXsWzz77rFl7dHQ0UlNT8dVXX2Hz5s2orKxEXFwcampquj1WcnIy1Gq1afPz8+vFtyQiIhpYVxta8Yv/+xbbj5VAIgH+cH8Y3vz5RL5OjExE/3/CrQsqCoLQ5SKLBoMBjz/+OFavXo3Q0NBeHXvLli2IiIjA9OnTzdoTEhLws5/9DBMmTMDcuXOxZ88eAMCHH37Y7bFWrFgBrVZr2kpLS3tVAxER0UA5XVaPh/5+GCcu12GUUo73n5yG5++5i4sTkxnRJlS4ublBJpN1GqWrqqrqNJoHAA0NDTh58iRycnLwwgsvAACMRiMEQYBcLkdGRgZmz55t6t/U1ISdO3dizZo1t63FwcEBEyZMQH5+frd9bG1tYWtr29uvR0RENKA+yynHH9NPo7XdiGB3B2xaHIVgd0exy6JhSLRwp1AoEBkZiczMTDzyyCOm9szMTDz88MOd+qtUKpw5c8asLSUlBV9//TV27dqFoCDz1bc/+eQTtLa24oknnrhtLa2trTh//jxmzpzZz29DREQ0OFrbDVj7n/P46NtiAMCcsR54a+FkqJR8vo66JupSKElJSVi0aBGioqIQGxuLTZs2oaSkBEuWLAHQcSu0vLwcqampkEqliIiIMNvfw8MDSqWyUzvQcUt2/vz5cHV17fTZsmXL8OCDD8Lf3x9VVVVYu3YtdDodnnzyycH5okRERP1QUtOE57dn42x5xyS+386+C7+fGwqplLdhqXuihrvExETU1NRgzZo10Gg0iIiIwN69exEQEAAA0Gg0KCkp6fNxL126hMOHDyMjI6PLz8vKyvDYY4+huroa7u7uiImJwbfffms6LxERkdi+PFuJl3adQkNLO5ztbfBW4mTcE+YhdllkASSCIAhiF2GJdDod1Go1tFotVCqV2OUQEZGV0Lcb8f++uID3jxQBACIDnPHOY1Pg42QncmUktt5mD75bloiIaJj4vqoRv0/LxZlyLQDguVljsOy+MNjIRF/cgiwIwx0REZHIBEHAx8dK8Jc9eWhpM8LJ3gZvPDoJP+b7YakfGO6IiIhEdLWhFX/YdQr7L14FAMwMccMbj06Cl1opcmVkqRjuiIiIRJKZdwXL00+j5poeCrkUy+8fi6fiAjkblu4Iwx0REdEQq72mx+p/n8O/cisAAOO8VfjbwskI9RwlcmVkDRjuiIiIhoggCNhzRoNV/zqHmmt6SCXA/8wag6Qfh8JWLhO7PLISDHdERERDoErXglc+O4uMvCsAgDDPUVj36ERM8nMStzCyOgx3REREg8hoFLDjRAle/+ICdC3tkEsl+M29d+E3994FhZxLnNDAY7gjIiIaJKfL6rHys7M4Vdaxbt1EXzXWPToRY724+D0NHoY7IiKiAaZtasMbGRew7VgJBAEYZStHUnwoFsUEQM4FiWmQMdwRERENEINRQHp2GV7/8gJqrukBAPMn++B/542Dh4rr1tHQYLgjIiIaAIfzq/GXvedxXqMDAIR4OGLNwxGIDXYVuTIaaRjuiIiI7sD3VQ14be8FfH2hCgAwSinH72aH4KkZgXwnLImC4Y6IiKgfqnQtePvrfOw4XgqDUYBcKsETMQF4cU4InB0UYpdHIxjDHRERUR/UXtPjH98UIPXoZbS0GQEA8eM9sTxhLMa4O4pcHRHDHRERUa9om9vwf4cK8f7hIlzTGwAAU/2d8If7xyJmDJ+ro+GD4Y6IiKgH9U16bM26jPcPF0HX0g4AiBitwv8XH4Z7Qt0hkUhErpDIHMMdERFRFzTaZmw5VITtx0vQdH2kLtTTEUk/DsN94Z4MdTRsMdwRERHdpOBqIzZ9U4jdOWVoMwgAgPHeKvz6nmDMm+ANmZShjoY3hjsiIhrxBEHA0YIabM26jMzzVyB0ZDpMD3LB8/cE427efiULwnBHREQjVpO+HZ/mlOPDrMu4dKXR1D53nAd+fU8wIgNcRKyOqH8Y7oiIaMS5XH0N24+XYOfxEtMkCXuFDD+dOhpPxgYixHOUyBUS9R/DHRERjQgtbQZ8cVaDncdLcayo1tTu72KPxbEB+HmUH9R2NiJWSDQwGO6IiMhqCYKAcxU67DxRgn/lVqDh+iidRALMCnHH4tgA3BPmwUkSZFUY7oiIyOoU11zD57kV+PxUBfKrfniWztfZDgui/PBopC98nOxErJBo8Ij+RuOUlBQEBQVBqVQiMjIShw4d6tV+R44cgVwux+TJk83at27dColE0mlraWkZkPMSEdHwVKVrwfuHi/Dwu0dw9xsH8NfMS8ivaoRCJsWDk3zw8TPROPjSvfjdnBAGO7Jqoo7cpaWlYenSpUhJScGMGTPw3nvvISEhAXl5efD39+92P61Wi8WLF2POnDm4cuVKp89VKhUuXrxo1qZUKu/4vERENLyU1jYhM+8KMvIqcbyoFsbrS5hIJcCMu9zw4CQf3BfuxWfpaESRCMKN1XyGXnR0NKZOnYqNGzea2saNG4f58+cjOTm52/0WLlyIkJAQyGQyfPbZZ8jNzTV9tnXrVixduhT19fUDft6b6XQ6qNVqaLVaqFSqXu1DRER35sYzdBl5V5CZdwXnNTqzz6f6O+GhST54YKIP3EfZilQl0eDobfYQbeROr9cjOzsby5cvN2uPj49HVlZWt/t98MEHKCgowMcff4y1a9d22aexsREBAQEwGAyYPHky/vznP2PKlCl3dN7W1la0traaftbpdN32JSKigdPSZsDRwhocuFCFfeerUF7fbPpMKulYaPjH470QP94Tfi72IlZKNDyIFu6qq6thMBjg6elp1u7p6YnKysou98nPz8fy5ctx6NAhyOVdlz527Fhs3boVEyZMgE6nw9/+9jfMmDEDp06dQkhISL/OCwDJyclYvXp1H78lERH1lSAIKKy+hm8uXsWBS1dxrLAGre1G0+dKGynuDnXHj8d7YfZYD7g4KESslmj4EX227K2vcxEEoctXvBgMBjz++ONYvXo1QkNDuz1eTEwMYmJiTD/PmDEDU6dOxTvvvIO33367z+e9YcWKFUhKSjL9rNPp4Ofn1/0XIyKiXtM2teHbohocyr+Kby5dRWlts9nn3mol7g51x9xxnvhRiBuUNjKRKiUa/kQLd25ubpDJZJ1Gy6qqqjqNqgFAQ0MDTp48iZycHLzwwgsAAKPRCEEQIJfLkZGRgdmzZ3faTyqVYtq0acjPz+/XeW+wtbWFrS2f3yAiGghN+nYcL6rF0YIaZBXU4GyFFjc/Aa6QSTEtyBn3hHrg7jB3hHg48t2uRL0kWrhTKBSIjIxEZmYmHnnkEVN7ZmYmHn744U79VSoVzpw5Y9aWkpKCr7/+Grt27UJQUFCX5xEEAbm5uZgwYUK/zktERHeupc2A3NJ6ZBXU4GhBNXJL69FmMJ/PF+zugLhgN9wT5o6YMa5wsBX95hKRRRL1T05SUhIWLVqEqKgoxMbGYtOmTSgpKcGSJUsAdNwKLS8vR2pqKqRSKSIiIsz29/DwgFKpNGtfvXo1YmJiEBISAp1Oh7fffhu5ubl49913e31eIiK6M/VNemQX1+HE5TqcvFyL02Va6A1Gsz6jnewQF+yKGXe5ITbYFZ4qZTdHI6K+EDXcJSYmoqamBmvWrIFGo0FERAT27t2LgIAAAIBGo0FJSUmfjllfX49f/epXqKyshFqtxpQpU3Dw4EFMnz691+clIqLeEwQBZXXNOFlcawpzl640durn5miL2GBXzAh2RVywG/xc7HirlWgQiLrOnSXjOndENFK1tBlwXqNDbmk9sovrcPJyHSp1LZ36jXF3wLQAF0QFOmNaoAsCXO0Z5ojuwLBf546IiIY/o7FjWZJTpfU4VVaP3NJ6nNfoOj0vJ5dKMMFXjWmBLogMcEZUgDNcHTkJjUgMDHdERGRS1dCC3JKOIHeqVItTZfVoaGnv1M/VQYHJfk6Y7OeEaUEumOTrBDsFlychGg4Y7oiIRihdSxvOletw+vqI3KnSelRoO99eVdpIMWG0GpN8nTDZ3wmTfJ3g68zn5YiGK4Y7IqIRoKaxFecqdDhbocW5ch3OVWhxuaapUz+JBAj1GIVJfmpM9nPGJD81Qj1HwUYmFaFqIuoPhjsiIisiCAKu6FpxtlyLsxVanC3XIa9C2+WIHNCxHMlEXzUm+XWMyE3wVcOR68sRWTT+CSYislCCIKC0tvl6iNPiXEXHiFx1o77L/kFuDgj3USFitBoRPmqE+6jgzPeyElkdhjsiIgtgMAooqm7E2XKdKcidrdB2OdlBKgFCPEYhfLQKET5qRIxWY5z3KIxS2ohQORENNYY7IqJhRt9uRH5VA86VX39GrkKHvAodmtsMnfoqZFKEeY1CxGgVwq8HubFeo6C04cxVopGK4Y6ISETNegPOV+pw7qbRuEuVjZ1e1QUAdjYyjPdRIcJHhfDRHbdVQzxGQSHnZAci+gHDHRHRENE2teGc5ofZqucqdCi42ghjF+8JGqWUX7+l2vGMXLiPCkFujpBJufwIEfWM4Y6IaBBUNbSYQtzZch3OabQorW3usq+bo+3126qq6xMd1HzvKhH1G8MdEdEdqm5svf56Li3OlNXjbIUOVxtau+zr62xnmql6Y8KDh0o5xBUTkTVjuCMi6oNrre04U67F6euv58otrUd5fecROYkECHZ3vGk0ToXxPio42XPpESIaXAx3RETdaDMYcbGyAbml9aYwl1/V0OkZuRtBbpKvEyb6/rD0iL2Cf8US0dDj3zxERNdptM3ILq7Dd8X1yC2tw7kKHVrbO89a9VYrMcnXqeOtDn5qTBit5hpyRDRsMNwR0YjUZjAir0KH7OI6ZJfUIae4rstXdKmUctOruTr+yWfkiGh4Y7gjohGhurEV3xXX4buSenxXXIdTZfWdRuVkUgnGeY/CVH9nTPF3wmQ/ZwS62nPWKhFZFIY7IrI6BqOAS1caOm6xltThu+I6XK5p6tTPyd4GU/2dERnQEeYm+TrBwZZ/LRKRZePfYkRk8bTNbcgtrb/+vFwdckvr0dja+Z2roZ6OmOrvjKkBHYFujJsDR+WIyOow3BGRRREEAcU1TThxuRbfldQhu7gO+VWNEG6ZweqgkGHK9SA31d8JU/ycobbnpAcisn4Md0Q0rAmCgO+rGvFtUS2OF9XiWGENqrpYIDjA1R6RpjDnjDCvUXxVFxGNSAx3RDSsGIwCzmt0HUGuqAYnLteh9prerI9CJsUkP3XH7dXrgc7N0VakiomIhheGOyISVZvBiLPl2uthrhYnLteiocX8eTmljRSRAc6YHuiK6DEumOznBKWNTKSKiYiGN4Y7IhpSre0GnCrV4nhRDY4V1SK7uA5NeoNZH0dbOaICnTE9yAXRQa6YMFoNhVwqUsVERJZF9HCXkpKCN954AxqNBuHh4diwYQNmzpx52/2OHDmCu+++GxEREcjNzTW1b968GampqTh79iwAIDIyEq+99hqmT59u6vPqq69i9erVZsfz9PREZWXlwHwpIjJp0rcjp6Qexwo7wlxOaT30t6wv52Rvg2mBLoi+HubGeY+CXMYwR0TUH6KGu7S0NCxduhQpKSmYMWMG3nvvPSQkJCAvLw/+/v7d7qfVarF48WLMmTMHV65cMfvswIEDeOyxxxAXFwelUol169YhPj4e586dw+jRo039wsPDsW/fPtPPMhlv8RANhIaWNpwsrsOxwlocL6rB6TIt2m95Gaubo21HkBvjgulBLgj1GAUpJz8QEQ0IiSDcuoDA0ImOjsbUqVOxceNGU9u4ceMwf/58JCcnd7vfwoULERISAplMhs8++8xs5O5WBoMBzs7O+Pvf/47FixcD6Bi5u91+t6PT6aBWq6HVaqFSqfp9HCJLV3dNjxOXO56XO15Ui3MVWtyS5eCjViJ6jCumB3WEOa4vR0TUd73NHqKN3On1emRnZ2P58uVm7fHx8cjKyup2vw8++AAFBQX4+OOPsXbt2tuep6mpCW1tbXBxcTFrz8/Ph4+PD2xtbREdHY3XXnsNY8aM6fY4ra2taG39YfkFnU5323MTWaOqhhacKKrDsaIaHC+qxYXKhk59AlztER3kgulBrogOcoGvsx3DHBHREBEt3FVXV8NgMMDT09Osvadn3/Lz87F8+XIcOnQIcnnvSl++fDlGjx6NuXPnmtqio6ORmpqK0NBQXLlyBWvXrkVcXBzOnTsHV1fXLo+TnJzc6Tk9ImsnCALK6ppx/Pos1uOXa1F49Vqnfnd5OF4Pcx3PzHmplSJUS0REwDCYUHHrf80LgtDlf+EbDAY8/vjjWL16NUJDQ3t17HXr1mHHjh04cOAAlMofftkkJCSY/n3ChAmIjY1FcHAwPvzwQyQlJXV5rBUrVph9ptPp4Ofn16s6iCyF0Sggv6oRxy/X4sT1QKfRtpj1kUiAcV6q60HOBdOCXLjGHBHRMCJauHNzc4NMJus0SldVVdVpNA8AGhoacPLkSeTk5OCFF14AABiNRgiCALlcjoyMDMyePdvU/80338Rrr72Gffv2YeLEiT3W4uDggAkTJiA/P7/bPra2trC15S8wsi431pg7cbkWx4vqcLK4FvVNbWZ95FIJJviqMT3QBdOub3yNFxHR8CVauFMoFIiMjERmZiYeeeQRU3tmZiYefvjhTv1VKhXOnDlj1paSkoKvv/4au3btQlBQkKn9jTfewNq1a/HVV18hKirqtrW0trbi/PnzvVqChciS1V3Tm97HerK4DqfL6tHSZr4siZ2NDFMDnDAt0AXTA10w2d8J9grRB/mJiKiXRP0bOykpCYsWLUJUVBRiY2OxadMmlJSUYMmSJQA6boWWl5cjNTUVUqkUERERZvt7eHhAqVSata9btw4rV67E9u3bERgYaBoZdHR0hKOjIwBg2bJlePDBB+Hv74+qqiqsXbsWOp0OTz755BB9c6LBZzQKKKxuRHZxnWkr6OJ5ObWdDaZdXzB4WqALIkarYcM15oiILJao4S4xMRE1NTVYs2YNNBoNIiIisHfvXgQEBAAANBoNSkpK+nTMlJQU6PV6PProo2btq1atwquvvgoAKCsrw2OPPYbq6mq4u7sjJiYG3377rem8RJao3WDEuQodvr2+WPB3JXWdbrECwBh3B0QFOCPy+jbGzZFrzBERWRFR17mzZFznjsR2c5j7trAGJy7XobHV/J2stnIpJvk5mcLcFH9nuDgoRKqYiIjuxLBf546I+qY3YU6llGN6kCtixrggKtAF471VfCcrEdEIw3BHNEy1G4zI09wIcx1LkzT0EOZixrhinLcKMt5iJSIa0RjuiIYJo1HA+UodjhbU4GhBx9sfbg1zo5RyRDPMERFRDxjuiEQiCAIKrjYiq6AGWd/X4FhRDepumQDBMEdERH3FcEc0RARBQHFNE44WdozMHS2swdWGVrM+DgoZpgW5IC7YFbFj3DDeh2GOiIj6huGOaBCV1zebbrMeLahGxS2v8rKVSxEV6Iy4YDfEjHHFRF+uMUdERHeG4Y5ogAiCgNLaZhwrqsGJy7U4VlSL4pomsz42Mgmm+DkjNtgVscGumOLvBFu5TKSKiYjIGjHcEfWT0SjgUlUDThR1BLkTl2txRWd+m1UmlWDCaHXHbdZgV0QFuMBOwTBHRESDh+GOqJda2gw4V6HDycsdQe7E5Tpom80nQNjIJJjo64TpQR3vZY0KdMYopY1IFRMR0UjEcEfUBUEQUFbXjJzSenxXXIec0nrkVWjRZjB/oYu9QobIAGdMC+x4L+sUfycobTgyR0RE4mG4IwKga2nD2XItckvrkVPSsVU3tnbq5+qgwNQAZ0wPdMH0IBeM91FxAgQREQ0rDHc04tRe0+NsuRZnK7Q4V6HDuXItLt8y8QEA5FIJwn1UmOLvjCn+Tpjq7wxfZztIJFyahIiIhi+GO7JaLW0GFNc0oai6ERcrGzvCXLm203IkN4x2ssNkPydM8e/Ywn3UvMVKREQWh+GOLJogCLiia0Xh1UYUVF9D4dVGFF69hsLqRpTXNcModL1fkJsDwn1UiBitRoSPGuE+Kjg7KIa2eCIiokHAcEcW4VprO4qqr6HgaiOKqq+ZAlzR1Wu4pjd0u98oWznGuDsg2N0R4aPViPBRYbyPijNYiYjIajHc0bBhMAqoqG9GwU2jb4VXO4Jcpa7rW6lAx1pyfs52GOPuiDFuDh3/dHfAGHcHuDva8hk5IiIaURjuaMhpm9pQcH3U7eYAV1RzDfp2Y7f7uTgoroc3B7Mg5+9iD4WcM1aJiIgAhjsaJG0GI0pqm64HN/ORuJpr+m73U8ikCHSzR5CbeYALdneAkz2fiSMiIrodhjvqN0EQUN2oR+GN5+BumtBQUtuE9u5mMwDwVNlijJvjD6Nw7g4IdnPEaGc7yKS8jUpERNRfDHd0Wy1tBlyuuWY2CldQfQ1FVxuha2nvdj87G5kpvAW5OSDY3QFj3BwR5O4AR1v+X4+IiGgw8DcsAegYhavUtZgCXMHVH0biyuubIXQzCCeRdKwPd+MWavBNI3FeKiUnMxAREQ0xhrsRpknffv35t2soqGo0Bbii6mto6mFJEZVS/sPt0+sjcWPcHRDo6sCFfomIiIYRhjsr1GYwolLbYvYM3I3JDJpu3s4AdCwpEuBi32k26hh3B7g6KDgKR0REZAEY7iyMIAjQtbSjor7ZtJXXt6D8pp+v6Fq6fTMD8MOSIsHu5hMa/F3sYSPjkiJERESWjOFumKpv0iOnpB4ltU0ormlCSW0TSmubUF7fjMbW7icx3KCQSRHg2nkUjkuKEBERWTfRw11KSgreeOMNaDQahIeHY8OGDZg5c+Zt9zty5AjuvvtuREREIDc31+yz9PR0rFy5EgUFBQgODsZf/vIXPPLIIwNy3qFyukyLp7ee6PZzFwcFfJyUGO1kBx8nO9M/OzYl3BxsIeWSIkRERCOOqOEuLS0NS5cuRUpKCmbMmIH33nsPCQkJyMvLg7+/f7f7abVaLF68GHPmzMGVK1fMPjt69CgSExPx5z//GY888gg+/fRTLFiwAIcPH0Z0dPQdnXcoBbk5YKzXKAS42sPfpWPzu775qO1gp+AkBiIiIupMIgjdLXIx+KKjozF16lRs3LjR1DZu3DjMnz8fycnJ3e63cOFChISEQCaT4bPPPjMbuUtMTIROp8MXX3xharv//vvh7OyMHTt23NF5b6bT6aBWq6HVaqFSqXr7lYmIiIj6pbfZQ7Sn5/V6PbKzsxEfH2/WHh8fj6ysrG73++CDD1BQUIBVq1Z1+fnRo0c7HfO+++4zHbO/521tbYVOpzPbiIiIiIYb0cJddXU1DAYDPD09zdo9PT1RWVnZ5T75+flYvnw5tm3bBrm86zvKlZWVPR6zP+cFgOTkZKjVatPm5+d32+9IRERENNREX/fi1rXTBEHocj01g8GAxx9/HKtXr0ZoaOgdH7O3571hxYoV0Gq1pq20tLTHGoiIiIjEINqECjc3N8hksk6jZVVVVZ1G1QCgoaEBJ0+eRE5ODl544QUAgNFohCAIkMvlyMjIwOzZs+Hl5dXjMft63htsbW1ha2vbr+9KRERENFREG7lTKBSIjIxEZmamWXtmZibi4uI69VepVDhz5gxyc3NN25IlSxAWFobc3FzTTNjY2NhOx8zIyDAds6/nJSIiIrIkoi6FkpSUhEWLFiEqKgqxsbHYtGkTSkpKsGTJEgAdt0LLy8uRmpoKqVSKiIgIs/09PDygVCrN2l988UXMmjULr7/+Oh5++GH861//wr59+3D48OFen5eIiIjIUoka7hITE1FTU4M1a9ZAo9EgIiICe/fuRUBAAABAo9GgpKSkT8eMi4vDzp078corr2DlypUIDg5GWlqaaWSvN+clIiIislSirnNnybjOHREREQ2lYb/OHRERERENPIY7IiIiIivCcEdERERkRUSdUGHJbjyqyNeQERER0VC4kTluN12C4a6fGhoaAICvISMiIqIh1dDQALVa3e3nnC3bT0ajERUVFRg1alSPry27EzqdDn5+figtLeWMXBHxOgwfvBbDB6/F8MDrMHwMxbUQBAENDQ3w8fGBVNr9k3UcuesnqVQKX1/fITmXSqXiH9phgNdh+OC1GD54LYYHXofhY7CvRU8jdjdwQgURERGRFWG4IyIiIrIiDHfDmK2tLVatWgVbW1uxSxnReB2GD16L4YPXYnjgdRg+htO14IQKIiIiIivCkTsiIiIiK8JwR0RERGRFGO6IiIiIrAjDHREREZEVYbgTWUpKCoKCgqBUKhEZGYlDhw712P+bb75BZGQklEolxowZg3/84x9DVKl168t12L17N3784x/D3d0dKpUKsbGx+Oqrr4awWuvW1z8TNxw5cgRyuRyTJ08e3AJHkL5ei9bWVrz88ssICAiAra0tgoOD8f777w9Rtdarr9dh27ZtmDRpEuzt7eHt7Y2nn34aNTU1Q1StdTp48CAefPBB+Pj4QCKR4LPPPrvtPqL+vhZINDt37hRsbGyEzZs3C3l5ecKLL74oODg4CMXFxV32LywsFOzt7YUXX3xRyMvLEzZv3izY2NgIu3btGuLKrUtfr8OLL74ovP7668Lx48eFS5cuCStWrBBsbGyE7777bogrtz59vRY31NfXC2PGjBHi4+OFSZMmDU2xVq4/1+Khhx4SoqOjhczMTKGoqEg4duyYcOTIkSGs2vr09TocOnRIkEqlwt/+9jehsLBQOHTokBAeHi7Mnz9/iCu3Lnv37hVefvllIT09XQAgfPrppz32F/v3NcOdiKZPny4sWbLErG3s2LHC8uXLu+z/hz/8QRg7dqxZ23PPPSfExMQMWo0jQV+vQ1fGjx8vrF69eqBLG3H6ey0SExOFV155RVi1ahXD3QDp67X44osvBLVaLdTU1AxFeSNGX6/DG2+8IYwZM8as7e233xZ8fX0HrcaRpjfhTuzf17wtKxK9Xo/s7GzEx8ebtcfHxyMrK6vLfY4ePdqp/3333YeTJ0+ira1t0Gq1Zv25DrcyGo1oaGiAi4vLYJQ4YvT3WnzwwQcoKCjAqlWrBrvEEaM/1+Lzzz9HVFQU1q1bh9GjRyM0NBTLli1Dc3PzUJRslfpzHeLi4lBWVoa9e/dCEARcuXIFu3btwgMPPDAUJdN1Yv++lg/6GahL1dXVMBgM8PT0NGv39PREZWVll/tUVlZ22b+9vR3V1dXw9vYetHqtVX+uw63++te/4tq1a1iwYMFglDhi9Oda5OfnY/ny5Th06BDkcv51NlD6cy0KCwtx+PBhKJVKfPrpp6iursbzzz+P2tpaPnfXT/25DnFxcdi2bRsSExPR0tKC9vZ2PPTQQ3jnnXeGomS6Tuzf1xy5E5lEIjH7WRCETm23699VO/VNX6/DDTt27MCrr76KtLQ0eHh4DFZ5I0pvr4XBYMDjjz+O1atXIzQ0dKjKG1H68ufCaDRCIpFg27ZtmD59OubNm4f169dj69atHL27Q325Dnl5efjd736HP/3pT8jOzsaXX36JoqIiLFmyZChKpZuI+fua/6krEjc3N8hksk7/9VVVVdUp7d/g5eXVZX+5XA5XV9dBq9Wa9ec63JCWloZnnnkG//znPzF37tzBLHNE6Ou1aGhowMmTJ5GTk4MXXngBQEfAEAQBcrkcGRkZmD179pDUbm368+fC29sbo0ePhlqtNrWNGzcOgiCgrKwMISEhg1qzNerPdUhOTsaMGTPw0ksvAQAmTpwIBwcHzJw5E2vXruUdniEi9u9rjtyJRKFQIDIyEpmZmWbtmZmZiIuL63Kf2NjYTv0zMjIQFRUFGxubQavVmvXnOgAdI3ZPPfUUtm/fzmdZBkhfr4VKpcKZM2eQm5tr2pYsWYKwsDDk5uYiOjp6qEq3Ov35czFjxgxUVFSgsbHR1Hbp0iVIpVL4+voOar3Wqj/XoampCVKp+a92mUwG4IeRIxp8ov++HpJpG9SlG1Pct2zZIuTl5QlLly4VHBwchMuXLwuCIAjLly8XFi1aZOp/Y2r173//eyEvL0/YsmULl0IZAH29Dtu3bxfkcrnw7rvvChqNxrTV19eL9RWsRl+vxa04W3bg9PVaNDQ0CL6+vsKjjz4qnDt3Tvjmm2+EkJAQ4dlnnxXrK1iFvl6HDz74QJDL5UJKSopQUFAgHD58WIiKihKmT58u1lewCg0NDUJOTo6Qk5MjABDWr18v5OTkmJakGW6/rxnuRPbuu+8KAQEBgkKhEKZOnSp88803ps+efPJJ4e677zbrf+DAAWHKlCmCQqEQAgMDhY0bNw5xxdapL9fh7rvvFgB02p588smhL9wK9fXPxM0Y7gZWX6/F+fPnhblz5wp2dnaCr6+vkJSUJDQ1NQ1x1danr9fh7bffFsaPHy/Y2dkJ3t7ewi9+8QuhrKxsiKu2Lvv37+/x7/3h9vtaIggcpyUiIiKyFnzmjoiIiMiKMNwRERERWRGGOyIiIiIrwnBHREREZEUY7oiIiIisCMMdERERkRVhuCMiIiKyIgx3RERERFaE4Y6IiIjIijDcEREREVkRhjsioiFw9epVeHl54bXXXjO1HTt2DAqFAhkZGSJWRkTWhu+WJSIaInv37sX8+fORlZWFsWPHYsqUKXjggQewYcMGsUsjIivCcEdENIR+85vfYN++fZg2bRpOnTqFEydOQKlUil0WEVkRhjsioiHU3NyMiIgIlJaW4uTJk5g4caLYJRGRleEzd0REQ6iwsBAVFRUwGo0oLi4WuxwiskIcuSMiGiJ6vR7Tp0/H5MmTMXbsWKxfvx5nzpyBp6en2KURkRVhuCMiGiIvvfQSdu3ahVOnTsHR0RH33nsvRo0ahf/85z9il0ZEVoS3ZYmIhsCBAwewYcMGfPTRR1CpVJBKpfjoo49w+PBhbNy4UezyiMiKcOSOiIiIyIpw5I6IiIjIijDcEREREVkRhjsiIiIiK8JwR0RERGRFGO6IiIiIrAjDHREREZEVYbgjIiIisiIMd0RERERWhOGOiIiIyIow3BERERFZEYY7IiIiIivCcEdERERkRf5/CxZOtT+qrUEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_advection_model(pinn.backbone)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('tf2-pinn')",
   "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"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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