cleanup of notebooks
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NT
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@ -46,10 +46,7 @@
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
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" InsecureRequestWarning)\n"
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"text": []
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"name": "stdout",
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@ -1129,7 +1126,7 @@
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"name": "bayesian-code-jul30upload.ipynb",
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"name": "bayesian-code.ipynb",
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"provenance": []
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},
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"kernelspec": {
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@ -19,7 +19,7 @@
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"phiflow directly gives us a sequence of differentiable operations, provided that we don't use the _numpy_ backend.\n",
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"The important step here is to include `phi.tf.flow` instad of `phi.flow` (for _pytorch_ you could use `phi.torch.flow`).\n",
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"\n",
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"So, as a first step, let's set up some constants, and initialize our domain with 128 points. We'll directly allocate a `velocity` field, and our constraint at $t=0.5$ (step 16), now as a `scalar_grid` in phiflow.\n"
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"So, as a first step, let's set up some constants, and initialize a `velocity` field with zeros, and our constraint at $t=0.5$ (step 16), now as a `CenteredGrid` in phiflow. Both are using periodic boundary conditions (via `extrapolation.PERIODIC`) and a spatial discretization of $\\Delta x = 1/128$.\n"
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]
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{
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@ -28,8 +28,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install --upgrade --quiet git+https://github.com/tum-pbs/PhiFlow@develop\n",
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"#!pip install --upgrade --quiet phiflow\n",
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"!pip install --upgrade --quiet phiflow \n",
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"from phi.tf.flow import *\n",
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"\n",
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"N = 128\n",
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@ -37,12 +36,12 @@
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"DT = 1./STEPS\n",
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"NU = 0.2/np.pi\n",
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"\n",
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"DOMAIN = Domain(x=N, boundaries=PERIODIC, bounds=Box[-1:1])\n",
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"# allocate velocity grid\n",
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"velocity = CenteredGrid(0, extrapolation.PERIODIC, x=N, bounds=Box[-1:1])\n",
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"\n",
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"# initialization of velocities\n",
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"velocity = DOMAIN.scalar_grid() # start from zero\n",
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"\n",
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"SOLUTION_T16 = DOMAIN.scalar_grid(np.asarray( [0.008612174447657694, 0.02584669669548606, 0.043136357266407785, 0.060491074685516746, 0.07793926183951633, 0.0954779141740818, 0.11311894389663882, 0.1308497114054023, 0.14867023658641343, 0.1665634396808965, 0.18452263429574314, 0.20253084411376132, 0.22057828799835133, 0.23865132431365316, 0.25673879161339097, 0.27483167307082423, 0.2929182325574904, 0.3109944766354339, 0.3290477753208284, 0.34707880794585116, 0.36507311960102307, 0.38303584302507954, 0.40094962955534186, 0.4188235294008765, 0.4366357052408043, 0.45439856841363885, 0.4720845505219581, 0.4897081943759776, 0.5072391070000235, 0.5247011051514834, 0.542067187709797, 0.5593576751669057, 0.5765465453632126, 0.5936507311857876, 0.6106452944663003, 0.6275435911624945, 0.6443221318186165, 0.6609900633731869, 0.67752574922899, 0.6939334022562877, 0.7101938106059631, 0.7263049537163667, 0.7422506131457406, 0.7580207366534812, 0.7736033721649875, 0.7889776974379873, 0.8041371279965555, 0.8190465276590387, 0.8337064887158392, 0.8480617965162781, 0.8621229412131242, 0.8758057344502199, 0.8891341984763013, 0.9019806505391214, 0.9143881632159129, 0.9261597966464793, 0.9373647624856912, 0.9476871303793314, 0.9572273019669029, 0.9654367940878237, 0.9724097482283165, 0.9767381835635638, 0.9669484658390122, 0.659083299684951, -0.659083180712816, -0.9669485121167052, -0.9767382069792288, -0.9724097635533602, -0.9654367970450167, -0.9572273263645859, -0.9476871280825523, -0.9373647681120841, -0.9261598056102645, -0.9143881718456056, -0.9019807055316369, -0.8891341634240081, -0.8758057205293912, -0.8621229450911845, -0.8480618138204272, -0.833706571569058, -0.8190466131476127, -0.8041372124868691, -0.7889777195422356, -0.7736033858767385, -0.758020740007683, -0.7422507481169578, -0.7263049162371344, -0.7101938950789042, -0.6939334061553678, -0.677525822052029, -0.6609901538934517, -0.6443222327338847, -0.6275436932970322, -0.6106454472814152, -0.5936507836778451, -0.5765466491708988, -0.5593578078967361, -0.5420672759411125, -0.5247011730988912, -0.5072391580614087, -0.4897082914472909, -0.47208460952428394, -0.4543985995006753, -0.4366355580500639, -0.41882350871539187, -0.40094955631843376, -0.38303594105786365, -0.36507302109186685, -0.3470786936847069, -0.3290476440540586, -0.31099441589505206, -0.2929180880304103, -0.27483158663081614, -0.2567388003912687, -0.2386513127155433, -0.22057831776499126, -0.20253089403524566, -0.18452269630486776, -0.1665634500729787, -0.14867027528284874, -0.13084990929476334, -0.1131191325854089, -0.09547794429803691, -0.07793928430794522, -0.06049114408297565, -0.0431364527809777, -0.025846763281087953, -0.00861212501518312] ))\n"
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"# and a grid with the reference solution \n",
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"REFERENCE_DATA = math.tensor([0.008612174447657694, 0.02584669669548606, 0.043136357266407785, 0.060491074685516746, 0.07793926183951633, 0.0954779141740818, 0.11311894389663882, 0.1308497114054023, 0.14867023658641343, 0.1665634396808965, 0.18452263429574314, 0.20253084411376132, 0.22057828799835133, 0.23865132431365316, 0.25673879161339097, 0.27483167307082423, 0.2929182325574904, 0.3109944766354339, 0.3290477753208284, 0.34707880794585116, 0.36507311960102307, 0.38303584302507954, 0.40094962955534186, 0.4188235294008765, 0.4366357052408043, 0.45439856841363885, 0.4720845505219581, 0.4897081943759776, 0.5072391070000235, 0.5247011051514834, 0.542067187709797, 0.5593576751669057, 0.5765465453632126, 0.5936507311857876, 0.6106452944663003, 0.6275435911624945, 0.6443221318186165, 0.6609900633731869, 0.67752574922899, 0.6939334022562877, 0.7101938106059631, 0.7263049537163667, 0.7422506131457406, 0.7580207366534812, 0.7736033721649875, 0.7889776974379873, 0.8041371279965555, 0.8190465276590387, 0.8337064887158392, 0.8480617965162781, 0.8621229412131242, 0.8758057344502199, 0.8891341984763013, 0.9019806505391214, 0.9143881632159129, 0.9261597966464793, 0.9373647624856912, 0.9476871303793314, 0.9572273019669029, 0.9654367940878237, 0.9724097482283165, 0.9767381835635638, 0.9669484658390122, 0.659083299684951, -0.659083180712816, -0.9669485121167052, -0.9767382069792288, -0.9724097635533602, -0.9654367970450167, -0.9572273263645859, -0.9476871280825523, -0.9373647681120841, -0.9261598056102645, -0.9143881718456056, -0.9019807055316369, -0.8891341634240081, -0.8758057205293912, -0.8621229450911845, -0.8480618138204272, -0.833706571569058, -0.8190466131476127, -0.8041372124868691, -0.7889777195422356, -0.7736033858767385, -0.758020740007683, -0.7422507481169578, -0.7263049162371344, -0.7101938950789042, -0.6939334061553678, -0.677525822052029, -0.6609901538934517, -0.6443222327338847, -0.6275436932970322, -0.6106454472814152, -0.5936507836778451, -0.5765466491708988, -0.5593578078967361, -0.5420672759411125, -0.5247011730988912, -0.5072391580614087, -0.4897082914472909, -0.47208460952428394, -0.4543985995006753, -0.4366355580500639, -0.41882350871539187, -0.40094955631843376, -0.38303594105786365, -0.36507302109186685, -0.3470786936847069, -0.3290476440540586, -0.31099441589505206, -0.2929180880304103, -0.27483158663081614, -0.2567388003912687, -0.2386513127155433, -0.22057831776499126, -0.20253089403524566, -0.18452269630486776, -0.1665634500729787, -0.14867027528284874, -0.13084990929476334, -0.1131191325854089, -0.09547794429803691, -0.07793928430794522, -0.06049114408297565, -0.0431364527809777, -0.025846763281087953, -0.00861212501518312] , math.spatial('x'))\n",
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"SOLUTION_T16 = CenteredGrid( REFERENCE_DATA, extrapolation.PERIODIC, x=N, bounds=Box[-1:1])"
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]
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},
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{
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"\n",
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" grad = math.gradients(loss, velocity.values)\n",
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"\n",
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"print('Loss: %f' % (loss))\n",
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"\n"
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"print('Loss: %f' % (loss))"
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]
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},
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{
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"Optimization step 30, loss: 0.008643\n",
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"Optimization step 35, loss: 0.005733\n",
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"Optimization step 40, loss: 0.003848\n",
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"Runtime 47.50s\n"
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"Runtime 204.01s\n"
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]
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}
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],
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Thinking back to the PINN version from {doc}`diffphys-code-burgers`, we have a much lower error here after only 50 steps (by ca. an order of magnitude), and the overall runtime is also much lower (ca. a factor of 4, depending on your hardware).\n",
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"Thinking back to the PINN version from {doc}`diffphys-code-burgers`, we have a much lower error here after only 50 steps (by ca. an order of magnitude), and the runtime is also lower (roughly by a factor of 2).\n",
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"\n",
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"Let's plot again how well our solution at $t=0.5$ (blue) matches the constraints (green) now:"
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]
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "Forw Simulations with Φ-Flow.ipynb",
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"name": "overview-ns-forw.ipynb",
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"provenance": []
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},
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"kernelspec": {
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],
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"metadata": {
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"colab": {
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"name": "physicalloss-code-may17b.ipynb",
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"name": "physicalloss-code.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"name": "reinflearn-code-aug1.ipynb",
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"name": "reinflearn-code.ipynb",
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"provenance": [],
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"collapsed_sections": []
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},
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],
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"metadata": {
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"colab": {
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"name": "supervised-airfoils-may16a.ipynb",
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"name": "supervised-airfoils.ipynb",
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"provenance": []
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},
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"kernelspec": {
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