484 lines
34 KiB
Plaintext
484 lines
34 KiB
Plaintext
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{
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"cells": [
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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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"source": [
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"import numpy as np\n",
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"import torch\n",
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"import igl\n",
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"import time\n",
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"\n",
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"import meshplot as mp\n",
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"import sys as _sys\n",
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"_sys.path.append(\"../src\")\n",
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"from elasticenergy import *\n",
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"from elasticsolid import *\n",
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"from adjoint_sensitivity import *\n",
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"from vis_utils import *\n",
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"from objectives import *\n",
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"from harmonic_interpolator import *\n",
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"from shape_optimizer import *\n",
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"\n",
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"from utils import *\n",
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"\n",
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"shadingOptions = {\n",
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" \"flat\":True,\n",
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" \"wireframe\":False, \n",
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"}\n",
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"\n",
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"rot = np.array(\n",
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" [[1, 0, 0 ],\n",
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" [0, 0, 1],\n",
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" [0, -1, 0 ]]\n",
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")\n",
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"\n",
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"torch.set_default_dtype(torch.float64)\n",
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"\n",
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"def to_numpy(tensor):\n",
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" return tensor.detach().clone().numpy()"
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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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"# Create the deformed object\n",
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"\n",
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"## Load the mesh"
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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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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "732bc7276b654a1db0b501b750c8fd87",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Renderer(camera=PerspectiveCamera(children=(DirectionalLight(color='white', intensity=0.6, position=(5.0, 1.0,…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<meshplot.Viewer.Viewer at 0x7f4f18719760>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vNP, _, _, tNP, _, _ = igl.read_obj(\"../data/beam.obj\")\n",
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"\n",
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"aabb = np.max(vNP, axis=0) - np.min(vNP, axis=0)\n",
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"length_scale = np.mean(aabb)\n",
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"\n",
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"\n",
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"v, t = torch.tensor(vNP), torch.tensor(tNP)\n",
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"eNP = igl.edges(tNP)\n",
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"beNP = igl.edges(igl.boundary_facets(tNP))\n",
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"\n",
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"def get_boundary_and_interior(vlen, t):\n",
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" bv = np.unique(igl.boundary_facets(t))\n",
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" vIdx = np.arange(vlen)\n",
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" iv = vIdx[np.invert(np.in1d(vIdx, bv))]\n",
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" return bv, iv\n",
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"# Compute boundary vertices and interior vertex indices\n",
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"bvNP, ivNP = get_boundary_and_interior(v.shape[0], tNP)\n",
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"\n",
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"mp.plot(vNP @ rot.T, np.array(tNP), shading=shadingOptions)"
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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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"## Add some physical characteristics"
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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": 3,
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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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"Pinned vertices: [ 0 1 11 12 22 23 33 34 44 54 55 75 76 86 87 88 89]\n"
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]
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}
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],
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"source": [
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"rho = 131 # [kg.m-3]\n",
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"damping = 0.\n",
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"young = 5e7 # [Pa] \n",
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"poisson = 0.2\n",
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"\n",
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"# Find some of the lowest vertices and pin them\n",
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"minX = torch.min(v[:, 0])\n",
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"pin_idx = torch.arange(v.shape[0])[v[:, 0] < minX + 0.2*aabb[0]]\n",
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"vIdx = np.arange(v.shape[0])\n",
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"pin_idx = vIdx[np.in1d(vIdx, bvNP) & np.in1d(vIdx, pin_idx)]\n",
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"print(\"Pinned vertices: {}\".format(pin_idx))"
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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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"# Initial guess\n",
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"\n",
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"The idea is that we start deforming the mesh by inverting gravity."
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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": 4,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f4fe02d4e90b47cea101747098c5b3bd",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Renderer(camera=PerspectiveCamera(children=(DirectionalLight(color='white', intensity=0.6, position=(4.9017367…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Inverted gravity\n",
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"force_mass = torch.zeros(size=(3,))\n",
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"force_mass[2] = + rho * 9.81\n",
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"\n",
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"# Gravity going in the wrong direction\n",
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"\n",
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"ee = NeoHookeanElasticEnergy(young, poisson)\n",
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"\n",
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"v = HarmonicInterpolator(v, t, ivNP).interpolate(v[bvNP])\n",
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"solid_init = ElasticSolid(v, t, ee, rho=rho, pin_idx=pin_idx, f_mass=force_mass)\n",
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"\n",
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"solid_init.find_equilibrium()\n",
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"plot_torch_solid(solid_init, beNP, rot, length_scale)\n",
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"\n",
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"# Use these as initial guesses\n",
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"v_init_rest = solid_init.v_def.clone().detach()\n",
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"v_init_def = solid_init.v_rest.clone().detach()\n",
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"\n",
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"# v_init_rest = solid_init.v_rest.clone().detach()\n",
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"# v_init_def = solid_init.v_def.clone().detach()"
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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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"# Inverse design\n"
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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": 5,
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"metadata": {
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"scrolled": false
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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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"Initial objective: 1.6230e+00\n",
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"\n"
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]
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}
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],
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"source": [
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"force_mass = torch.zeros(size=(3,))\n",
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"force_mass[2] = - rho * 9.81\n",
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"use_linear = False\n",
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"\n",
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"# The target is the initial raw mesh\n",
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"vt_surf = torch.tensor(vNP[bvNP, :])\n",
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"\n",
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"# Create solid\n",
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"if use_linear:\n",
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" ee = LinearElasticEnergy(young, poisson)\n",
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"else:\n",
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" ee = NeoHookeanElasticEnergy(young, poisson)\n",
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"solid_ = ElasticSolid(v_init_rest, t, ee, rho=rho, pin_idx=pin_idx, f_mass=force_mass)\n",
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"solid_.update_def_shape(v_init_def)\n",
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"\n",
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"optimizer = ShapeOptimizer(solid_, vt_surf, weight_reg=10.)\n",
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"\n",
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"v_eq_init = optimizer.solid.v_def.clone().detach() #bookkeeping"
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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": 6,
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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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"Objective after 40 optimization step(s): 1.5788e+00\n",
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" Line search Iters: 9\n",
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"Elapsed time: 97.1s. \n",
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"Estimated remaining time: 0.0s\n",
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"\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "10cffc9c94334b8aa1529d4b6fb36278",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Renderer(camera=PerspectiveCamera(children=(DirectionalLight(color='white', intensity=0.6, position=(5.0444669…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"optimizer.optimize(step_size_init=1e-2, max_l_iter=10, n_optim_steps=40)"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
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"text/plain": [
|
||
|
|
"<Figure size 720x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
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|
},
|
||
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|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(10, 6))\n",
|
||
|
|
"plt.plot(to_numpy(optimizer.objectives[optimizer.objectives > 0]))\n",
|
||
|
|
"plt.title(\"Objective as optimization goes\", fontsize=14)\n",
|
||
|
|
"plt.xlabel(\"Optimization steps\", fontsize=12)\n",
|
||
|
|
"plt.ylabel(\"Objective\", fontsize=12)\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Green (Initial guess for rest state) deploys to Black\n",
|
||
|
|
"\n",
|
||
|
|
"Blue (Optimized rest state) deploys to Yellow\n",
|
||
|
|
"\n",
|
||
|
|
"Red is the Target Shape\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
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|
{
|
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "cc0e6ec0eb174783a516c85f67697225",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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||
|
|
"Renderer(camera=PerspectiveCamera(children=(DirectionalLight(color='white', intensity=0.6, position=(5.0444669…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
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|
"output_type": "display_data"
|
||
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|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"4"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"p = mp.plot(np.array(optimizer.solid.v_def) @ rot.T, tNP, shading=shadingOptions)\n",
|
||
|
|
"# p.add_points(np.array(optimizer.solid.v_def)[pin_idx, :] @ rot.T, shading={\"point_color\":\"black\", \"point_size\": 0.2})\n",
|
||
|
|
"p.add_edges(np.array(v_init_rest) @ rot.T, beNP, shading={\"line_color\": \"green\"})\n",
|
||
|
|
"p.add_edges(vNP @ rot.T, beNP, shading={\"line_color\": \"red\"})\n",
|
||
|
|
"p.add_edges(np.array(v_eq_init) @ rot.T, beNP, shading={\"line_color\": \"black\"})\n",
|
||
|
|
"p.add_edges(np.array(optimizer.solid.v_rest) @ rot.T, beNP, shading={\"line_color\": \"blue\"})\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"v_rest_optim_g = optimizer.solid.v_rest.clone().detach() #bookkeeping"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Add point load to the right most vertices\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"maxX = torch.min(v[:, 0])\n",
|
||
|
|
"f_point_idx = torch.arange(v.shape[0])[v[:, 0] > maxX - 0.01*aabb[0]]\n",
|
||
|
|
"\n",
|
||
|
|
"f_point = torch.zeros(size=(f_point_idx.shape[0], 3))\n",
|
||
|
|
"f_point[:, 2] = -5e4\n",
|
||
|
|
"\n",
|
||
|
|
"optimizer.solid.add_point_load(f_point_idx, f_point)\n",
|
||
|
|
"optimizer.set_params(optimizer.params)\n",
|
||
|
|
"v_def_optim_g_under_point = optimizer.solid.v_def.clone().detach() #bookkeeping"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Objective after 21 optimization step(s): 2.2335e+01\n",
|
||
|
|
" Line search Iters: 9\n",
|
||
|
|
"Elapsed time: 2215.2s. \n",
|
||
|
|
"Estimated remaining time: 8333.3s\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
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"model_id": "f0b258afc95d4bdf8e8bb0e3fb529d05",
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||
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|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
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||
|
|
"text/plain": [
|
||
|
|
"Renderer(camera=PerspectiveCamera(children=(DirectionalLight(color='white', intensity=0.6, position=(5.0945925…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"optimizer.reset_BFGS()\n",
|
||
|
|
"optimizer.optimize(step_size_init=1e-2, max_l_iter=10, n_optim_steps=100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Green (Optimum rest state under gravity) deploys to Black with the additional point load\n",
|
||
|
|
"\n",
|
||
|
|
"Blue (Optimized rest state) deploys to Yellow\n",
|
||
|
|
"\n",
|
||
|
|
"Red is the Target Shape\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"p = mp.plot(np.array(optimizer.solid.v_def) @ rot.T, tNP, shading=shadingOptions)\n",
|
||
|
|
"# p.add_points(np.array(optimizer.solid.v_def)[pin_idx, :] @ rot.T, shading={\"point_color\":\"black\", \"point_size\": 0.2})\n",
|
||
|
|
"p.add_edges(np.array(v_rest_optim_g) @ rot.T, beNP, shading={\"line_color\": \"green\"})\n",
|
||
|
|
"p.add_edges(vNP @ rot.T, beNP, shading={\"line_color\": \"red\"})\n",
|
||
|
|
"p.add_edges(np.array(v_def_optim_g_under_point) @ rot.T, beNP, shading={\"line_color\": \"black\"})\n",
|
||
|
|
"p.add_edges(np.array(optimizer.solid.v_rest) @ rot.T, beNP, shading={\"line_color\": \"blue\"})\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.9.7"
|
||
|
|
},
|
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|
|
"toc": {
|
||
|
|
"base_numbering": 1,
|
||
|
|
"nav_menu": {},
|
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|
|
"number_sections": true,
|
||
|
|
"sideBar": true,
|
||
|
|
"skip_h1_title": false,
|
||
|
|
"title_cell": "Table of Contents",
|
||
|
|
"title_sidebar": "Contents",
|
||
|
|
"toc_cell": false,
|
||
|
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"toc_position": {},
|
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|
|
"toc_section_display": true,
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"toc_window_display": false
|
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|
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}
|
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|
|
},
|
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|
|
"nbformat": 4,
|
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|
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"nbformat_minor": 4
|
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|
|
}
|