updated SoL code to phiflow3.2
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@@ -123,33 +123,49 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"This notebook is running locally, please make sure the necessary pip packages are isntalled.\n"
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]
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}
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],
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"source": [
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"try:\n",
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" import google.colab # to ensure that we are inside colab\n",
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" !pip install --upgrade --quiet phiflow==3.2\n",
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" #!pip install --upgrade --quiet git+https://github.com/tum-pbs/PhiFlow@develop\n",
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" # NT_DEBUG , remove develop for pbdl-dataset:\n",
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" !pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset@develop\n",
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"\n",
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"except ImportError:\n",
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" print(\"This notebook is running locally, please make sure the necessary pip packages are installed.\")\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "JwZudtWauiGa",
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"outputId": "3e61b6dd-e1ea-47e7-f9af-6cde0c64162a"
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"outputId": "bb03fd1b-62e7-4657-fe80-046b11c9852d"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m182.2/182.2 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m306.1/306.1 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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"Using device: cuda:0\n"
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]
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}
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],
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"source": [
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"!pip install --upgrade --quiet phiflow==3.1\n",
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"!pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset@develop\n",
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"\n",
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"import os, sys, logging, argparse, pickle, glob, random, pylab, time\n",
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"from tqdm import tqdm\n",
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"from phi.torch.flow import *\n",
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@@ -175,7 +191,7 @@
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"id": "RY1F4kdWPLNG"
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},
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"source": [
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"And while we're at it, we also set the random seed - obviously, 42 is the ultimate choice here \ud83d\ude42"
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"And while we're at it, we also set the random seed - obviously, 42 is the ultimate choice here 🙂"
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]
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},
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{
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@@ -206,7 +222,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {
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"id": "6WNMcdWUw4EP"
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},
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@@ -218,7 +234,7 @@
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" def __init__(self, domain):\n",
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" self.domain = domain\n",
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"\n",
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" self.vel_BcMask = self.domain.staggered_grid(HardGeometryMask( Box(y=(None, 5), x=None) ) )\n",
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" self.vel_BcMask = self.domain.staggered_grid( HardGeometryMask( Box(y=(None, 5), x=None) ) )\n",
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"\n",
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" self.inflow = self.domain.scalar_grid(Box(y=(5,10), x=(25,75)) ) # scale with domain if necessary!\n",
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" self.obstacles = [Obstacle(Sphere(center=tensor([50, 50], channel(vector=\"y,x\")), radius=10))]\n",
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@@ -280,13 +296,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "qIrWYTy6xscA",
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"outputId": "90e91528-0f57-467a-a407-6f895bd8333b"
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"outputId": "209465bc-507e-4cd1-de90-2df940cda497"
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},
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"outputs": [
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{
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@@ -362,7 +378,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {
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"id": "hhGFpTjGyRyg"
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},
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@@ -421,7 +437,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {
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"id": "Dfwd4TnqN1Tn"
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},
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@@ -449,22 +465,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 7,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "59EBdEdj0QR2",
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"outputId": "16176f06-542c-4a65-8562-b0efd0da621d"
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"outputId": "3fb7f0f4-a515-4478-ec4d-18a329ad693b"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[Kdownload completed\t \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[38;5;240m\u001b[94m 100%\u001b[0m\n",
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"\u001b[96m\u001b[1mSuccess:\u001b[22m Loaded solver-in-the-loop-wake-flow with 6 simulations (6 selected) and 496 samples each.\u001b[0m\n",
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"\u001b[1mInfo:\u001b[22m No precomputed normalization data found (or not complete). Calculating data...\u001b[0m\n"
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"\u001b[Kdownload completed\t ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[38;5;240m\u001b[96m 100%\u001b[0m6m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\n",
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"\u001b[96m\u001b[1mSuccess:\u001b[22m Loaded solver-in-the-loop-wake-flow with 10 simulations (6 selected) and 496 samples each.\u001b[0m\n",
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"\u001b[1mInfo:\u001b[22m No precomputed normalization data found (or not complete). Calculating data...\u001b[0m\n",
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"[np.float64(1237.79296875), np.float64(1453.7359614526729)]\n"
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]
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}
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],
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@@ -473,12 +490,16 @@
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"import pbdl.torch.loader\n",
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"\n",
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"dataloader = pbdl.torch.loader.Dataloader(\"solver-in-the-loop-wake-flow\", MSTEPS, shuffle=True, sel_sims=[0,1,2,3,4,5],\n",
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" batch_size=BATCH_SIZE, normalize=\"std\", intermediate_time_steps=True)\n",
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" batch_size=BATCH_SIZE, normalize_const=\"std\", normalize_data=\"std\", intermediate_time_steps=True)\n",
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"\n",
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"# remember for normalization of the Reynolds number conditioning , then turn off (None), we'll only normalize Re manually below\n",
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"DATA_RE_MEAN = dataloader.dataset.normalize.const_mean[0][0]\n",
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"DATA_RE_STD = dataloader.dataset.normalize.const_std[0][0]\n",
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"dataloader.dataset.normalize = None"
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"# workaround for using un-normalized and normalized values in one script:\n",
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"# save the normalization constants of the Reynolds number conditioning , then turn off (norm=None);\n",
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"# the Re values will be normalized manually later on\n",
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"DATA_RE_MEAN = dataloader.dataset.norm_strat_const.const_mean[0][0]\n",
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"DATA_RE_STD = dataloader.dataset.norm_strat_const.const_std[0][0]\n",
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"print([DATA_RE_MEAN,DATA_RE_STD])\n",
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"dataloader.dataset.norm_strat_const = None\n",
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"dataloader.dataset.norm_strat_data = None"
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]
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},
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{
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@@ -494,16 +515,34 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "EjgkdCzKP2Ip",
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"outputId": "36d2d677-6293-4b7d-e7f2-c6f1a7343aa9",
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"outputId": "3ebef773-0d85-43de-9f78-c31719ccbd45",
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"scrolled": true
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},
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"outputs": [],
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"outputs": [
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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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"/tmp/ipykernel_774262/1641432920.py:8: FutureWarning: Domain (phi.physics._boundaries) is deprecated and will be removed in a future release.\n",
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"Please create grids directly, replacing the domain with a dict, e.g.\n",
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" domain = dict(x=64, y=128, bounds=Box(x=1, y=1))\n",
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" grid = CenteredGrid(0, **domain)\n",
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" from phi.physics._boundaries import Domain, OPEN, STICKY as CLOSED\n",
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"/tmp/ipykernel_774262/1641432920.py:15: DeprecationWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
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" domain = phi.physics._boundaries.Domain(y=SOURCE_RES[0], x=SOURCE_RES[1], boundaries=BNDS, bounds=Box(y=2*LENGTH, x=LENGTH))\n",
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"/tmp/ipykernel_774262/1641432920.py:15: FutureWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
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" domain = phi.physics._boundaries.Domain(y=SOURCE_RES[0], x=SOURCE_RES[1], boundaries=BNDS, bounds=Box(y=2*LENGTH, x=LENGTH))\n",
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"/tmp/ipykernel_774262/1529251970.py:7: DeprecationWarning: HardGeometryMask and SoftGeometryMask are deprecated. Use field.mask or field.resample instead.\n",
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" self.vel_BcMask = self.domain.staggered_grid( HardGeometryMask( Box(y=(None, 5), x=None) ) )\n"
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]
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}
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],
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"source": [
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"# this is the actual resolution in terms of cells for phiflow (not too crucial)\n",
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"SOURCE_RES = [64,32]\n",
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@@ -536,7 +575,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {
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"id": "5VCAzofU1FoH"
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},
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@@ -565,7 +604,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 10,
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"metadata": {
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"id": "KFp4eUta1FoH"
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},
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@@ -617,18 +656,40 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "AJn5a5k31FoH",
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"outputId": "91c710f7-1475-4dbb-8c33-45d33063859b",
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"outputId": "11f40f98-5554-4b6f-f436-c385d98534b0",
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"scrolled": true
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},
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"outputs": [],
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"outputs": [
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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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" 0%| | 0/4960 [00:00<?, ?it/s]/tmp/ipykernel_774262/4154370188.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" input = torch.tensor(input_cpu, dtype=torch.float32).to(device);\n",
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"/tmp/ipykernel_774262/4154370188.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" targets = torch.tensor(targets_cpu, dtype=torch.float32).to(device)\n",
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"/home/thuerey/anaconda3/envs/torch24/lib/python3.12/site-packages/phiml/math/_optimize.py:631: UserWarning: Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\n",
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" warnings.warn(\"Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\")\n",
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"/home/thuerey/anaconda3/envs/torch24/lib/python3.12/site-packages/phiml/backend/torch/_torch_backend.py:803: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n",
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" return torch.sparse_csr_tensor(row_pointers, column_indices, values, shape, device=values.device)\n",
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"/home/thuerey/anaconda3/envs/torch24/lib/python3.12/site-packages/phiml/math/_optimize.py:631: UserWarning: Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\n",
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" warnings.warn(\"Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\")\n",
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"loss 15.62617: 0%| | 1/4960 [00:12<16:36:58, 12.06s/it]/tmp/ipykernel_774262/4154370188.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" input = torch.tensor(input_cpu, dtype=torch.float32).to(device);\n",
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"/tmp/ipykernel_774262/4154370188.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" targets = torch.tensor(targets_cpu, dtype=torch.float32).to(device)\n",
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"loss 0.23079132: 100%|████████████████████████████████████| 4960/4960 [3:10:57<00:00, 2.31s/it]\n"
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]
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}
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],
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"source": [
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"EPOCHS = 1\n",
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"EPOCHS = 5\n",
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"\n",
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"pbar = tqdm(initial=0, total=EPOCHS*len(dataloader), ncols=96)\n",
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"\n",
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@@ -673,26 +734,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "RPhPR9-51FoI",
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"outputId": "6adee7c0-2117-4ec6-e731-b71532d4e819"
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"id": "RPhPR9-51FoI"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[Kdownload completed\t \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[38;5;240m\u001b[94m 100%\u001b[0m\n",
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"\u001b[Kdownload completed\t ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[38;5;240m\u001b[96m 100%\u001b[0m6m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\n",
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"\u001b[96m\u001b[1mSuccess:\u001b[22m Loaded solver-in-the-loop-wake-flow with 10 simulations (4 selected) and 300 samples each.\u001b[0m\n"
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]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#del dataloader # close hdf5 file handle\n",
|
||||
"del dataloader # close hdf5 file handle\n",
|
||||
"\n",
|
||||
"# get new & unseen test data\n",
|
||||
@@ -711,7 +767,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"id": "NRJwUnRz1FoI"
|
||||
},
|
||||
@@ -737,7 +793,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"id": "RWC0mKNJ1FoI"
|
||||
},
|
||||
@@ -803,20 +859,26 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "aA7BRsSZ1FoJ",
|
||||
"outputId": "fa436860-e5c9-4719-a605-50b9668fbe2d"
|
||||
"id": "aA7BRsSZ1FoJ"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Re \u001b[94m1171.875\u001b[0m\n"
|
||||
"Re \u001b[94m73.24219\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/tmp/ipykernel_774262/1514360431.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
|
||||
" input = torch.tensor(input_cpu, dtype=torch.float32).to(device)\n",
|
||||
"/tmp/ipykernel_774262/1514360431.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
|
||||
" targets = torch.tensor(targets_cpu, dtype=torch.float32).to(device)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -840,39 +902,61 @@
|
||||
"`ROLLOUT_STEPS` below determines the number of time steps to compute with both variants. The cell also directly outputs the mean relative error.\n",
|
||||
"Due to the stochastic training and numerical round-off errors, the results can vary slightly over different runs, but in general the hybrid solver with its Neural correction operator should show a significantly reduced numerical error: typically 5-6x lower than the low-fidelity solver.\n",
|
||||
"\n",
|
||||
"**This shows the central objective of this training setup has been achieved: the hybrid solver yields substantially reduced numerical errors and generalizes to new Reynolds numbers \ud83d\udc4d**"
|
||||
"**This shows the central objective of this training setup has been achieved: the hybrid solver yields substantially reduced numerical errors and generalizes to new Reynolds numbers 👍**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "QlsK9rG71FoJ",
|
||||
"outputId": "2005389a-32e1-49b9-cb46-3a7c6d2d425d"
|
||||
"id": "QlsK9rG71FoJ"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Running test with Re=\u001b[94m1171.875\u001b[0m, Sim. only\n"
|
||||
"Running test with Re=\u001b[94m73.24219\u001b[0m, Sim. only\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Sim. only: 0%| | 0/100 [00:00<?, ?it/s]/home/thuerey/anaconda3/envs/torch24/lib/python3.12/site-packages/phiml/math/_optimize.py:631: UserWarning: Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\n",
|
||||
" warnings.warn(\"Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\")\n",
|
||||
"/home/thuerey/anaconda3/envs/torch24/lib/python3.12/site-packages/phiml/math/_optimize.py:631: UserWarning: Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\n",
|
||||
" warnings.warn(\"Possible rank deficiency detected. Matrix might be singular which can lead to convergence problems. Please specify using Solve(rank_deficiency=...).\")\n",
|
||||
"Sim. only: 100%|██████████████| 100/100 [00:31<00:00, 3.14it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Running test with Re=\u001b[94m1171.875\u001b[0m, With corr.\n"
|
||||
"Running test with Re=\u001b[94m73.24219\u001b[0m, With corr.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"With corr.: 100%|█████████████| 100/100 [00:52<00:00, 1.89it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Rel. L2 errors: low-fidelity: 0.13708033837378025 corrected: 0.03381159909069538\n"
|
||||
"\n",
|
||||
" Rel. L2 errors: low-fidelity: 0.10863638424314559 corrected: 0.031211425131186844\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -895,26 +979,24 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 466
|
||||
},
|
||||
"id": "ys_qAMGu1FoK",
|
||||
"outputId": "02dc5fa1-62e9-4f24-cce6-051bd644c30e"
|
||||
"id": "ys_qAMGu1FoK"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"MAE for low-fidelity: 0.13708033837378025 , and hybrid: 0.03381159909069538\n"
|
||||
]
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.legend.Legend at 0x7de77dc7b770>"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
@@ -942,19 +1024,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 471
|
||||
},
|
||||
"id": "MZnqSzq21FoK",
|
||||
"outputId": "14037f18-44e4-4d3a-c0bd-0caeb384513b"
|
||||
"id": "MZnqSzq21FoK"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1600x500 with 8 Axes>"
|
||||
]
|
||||
@@ -1032,15 +1109,6 @@
|
||||
"* Use the external github code to generate tougher test data, and run your trained NN on these cases. You'll see that a reduced training error not always directly correlates with an improved test performance.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"id": "WyM-UoqA1FoL"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
@@ -1051,7 +1119,8 @@
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
@@ -1068,5 +1137,5 @@
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in new issue
Block a user