{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Descent for Finding Stellar Wind Parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# numerics\n", "import jax\n", "import jax.numpy as jnp\n", "import optax\n", "\n", "# plotting\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "# fluids\n", "from jf1uids._physics_modules._stellar_wind.stellar_wind_options import WindParams\n", "from jf1uids import strongest_shock_radius\n", "from jf1uids import SimulationConfig\n", "from jf1uids import get_helper_data\n", "from jf1uids import SimulationParams\n", "from jf1uids import time_integration\n", "from jf1uids import get_registered_variables\n", "from jf1uids.fluid_equations.fluid import construct_primitive_state\n", "\n", "\n", "# units\n", "from jf1uids import CodeUnits\n", "from astropy import units as u\n", "import astropy.constants as c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initiating the stellar wind simulation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "👷 Setting up simulation...\n" ] } ], "source": [ "from jf1uids.option_classes.simulation_config import BACKWARDS, SPHERICAL\n", "from jf1uids._physics_modules._stellar_wind.stellar_wind_options import WindConfig\n", "\n", "\n", "print(\"👷 Setting up simulation...\")\n", "\n", "# simulation settings\n", "gamma = 5/3\n", "\n", "# spatial domain\n", "geometry = SPHERICAL\n", "box_size = 1.0\n", "\n", "# ATTENTION: For testing we can take a lower resolution, the 3600 simulation\n", "# for the loss-map from the paper with the settings from the paper take\n", "# roughly 37 minutes and the gradient descents ~1h to run on the CPU\n", "# of an AMD EPYC 7452 32-Core Processor.\n", "\n", "fast_instead_paper = False\n", "\n", "if fast_instead_paper:\n", " # Lower resolution is faster\n", " # especially as then we can also\n", " # take larger timesteps\n", "\n", " # setting for quick testing\n", " num_cells = 101\n", " # num_timesteps = 4000\n", "else:\n", " # setting from the paper\n", " num_cells = 401\n", " # num_timesteps = 20000\n", "\n", "fixed_timestep = False\n", "\n", "differentiation_mode = BACKWARDS\n", "num_checkpoints = 1000\n", "\n", "\n", "# activate stellar wind\n", "stellar_wind = True\n", "\n", "# setup simulation config\n", "config = SimulationConfig(\n", " geometry = geometry,\n", " box_size = box_size, \n", " num_cells = num_cells,\n", " fixed_timestep = fixed_timestep,\n", " differentiation_mode = differentiation_mode,\n", " num_checkpoints = num_checkpoints,\n", " # num_timesteps = num_timesteps,\n", " wind_config = WindConfig(\n", " stellar_wind = stellar_wind,\n", " num_injection_cells = 10,\n", " ),\n", ")\n", "\n", "helper_data = get_helper_data(config)\n", "registered_variables = get_registered_variables(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting the simulation parameters and initial state" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For spherical geometry, only HLL is currently supported.\n", "Automatically setting reflective left and open right boundary for spherical geometry.\n", "For stellar wind simulations, we need source term aware timesteps, turning on.\n" ] } ], "source": [ "# code units\n", "\n", "from jf1uids.option_classes.simulation_config import finalize_config\n", "\n", "\n", "code_length = 3 * u.parsec\n", "code_mass = 1e-4 * u.M_sun\n", "code_velocity = 1 * u.km / u.s\n", "code_units = CodeUnits(code_length, code_mass, code_velocity)\n", "\n", "# time domain\n", "C_CFL = 0.8\n", "t_final = 2.5 * 1e4 * u.yr\n", "t_end = t_final.to(code_units.code_time).value\n", "dt_max = 0.1 * t_end # not so important if the timestep criterion is good\n", "\n", "params = SimulationParams(\n", " C_cfl = C_CFL,\n", " dt_max = dt_max,\n", " gamma = gamma,\n", " t_end = t_end\n", ")\n", "\n", "# homogeneous initial state\n", "rho_0 = 2 * c.m_p / u.cm**3\n", "p_0 = 3e4 * u.K / u.cm**3 * c.k_B\n", "\n", "rho_init = jnp.ones(num_cells) * rho_0.to(code_units.code_density).value\n", "u_init = jnp.zeros(num_cells)\n", "p_init = jnp.ones(num_cells) * p_0.to(code_units.code_pressure).value\n", "\n", "# get initial state\n", "initial_state = construct_primitive_state(\n", " config = config,\n", " registered_variables = registered_variables,\n", " density = rho_init,\n", " velocity_x = u_init,\n", " gas_pressure = p_init,\n", ")\n", "\n", "config = finalize_config(config, initial_state.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up a reference simulation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "sample_simulation = lambda velocity, mass_loss_rate: time_integration(initial_state, config, SimulationParams(\n", " C_cfl=params.C_cfl,\n", " dt_max=params.dt_max,\n", " gamma=params.gamma,\n", " t_end=params.t_end,\n", " wind_params=WindParams(\n", " wind_mass_loss_rate=mass_loss_rate,\n", " wind_final_velocity=velocity\n", " )\n", "), helper_data, registered_variables)\n", "\n", "# generate a reference simulation\n", "M_star = 40 * u.M_sun\n", "wind_final_velocity = 2000 * u.km / u.s\n", "wind_mass_loss_rate = 2.965e-3 / (1e6 * u.yr) * M_star\n", "\n", "reference_params = WindParams(\n", " wind_mass_loss_rate = wind_mass_loss_rate.to(code_units.code_mass / code_units.code_time).value,\n", " wind_final_velocity = wind_final_velocity.to(code_units.code_velocity).value\n", ")\n", "\n", "reference_simulation = sample_simulation(\n", " reference_params.wind_final_velocity,\n", " reference_params.wind_mass_loss_rate\n", ")\n", "\n", "reference_shock_radius = strongest_shock_radius(reference_simulation, helper_data, 10, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Losses" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def density_loss(vel_mass_loss):\n", " velocity = vel_mass_loss[0]\n", " mass_loss_rate = vel_mass_loss[1]\n", " final_state = sample_simulation(velocity, mass_loss_rate)\n", " return jnp.mean(jnp.abs(final_state[0] - reference_simulation[0]))\n", "\n", "def full_profile_loss(vel_mass_loss):\n", " velocity = vel_mass_loss[0]\n", " mass_loss_rate = vel_mass_loss[1]\n", " final_state = sample_simulation(velocity, mass_loss_rate)\n", " return jnp.sum(jnp.abs(final_state - reference_simulation))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loss map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_loss_map(velocity_range, mass_loss_rate_range):\n", " loss_map = jnp.zeros((len(velocity_range) * len(mass_loss_rate_range),))\n", " vel_list = jnp.zeros((len(velocity_range) * len(mass_loss_rate_range),))\n", " mass_list = jnp.zeros((len(velocity_range) * len(mass_loss_rate_range),))\n", " ind = 0\n", "\n", " for i, velocity in enumerate(velocity_range):\n", " for j, mass_loss_rate in enumerate(mass_loss_rate_range):\n", " loss_map = loss_map.at[ind].set(density_loss((velocity, mass_loss_rate)))\n", " vel_list = vel_list.at[ind].set(velocity)\n", " mass_list = mass_list.at[ind].set(mass_loss_rate)\n", " ind += 1\n", " print(f\"Done {ind}/{len(velocity_range) * len(mass_loss_rate_range)}\")\n", " return loss_map, vel_list, mass_list\n", "\n", "# generate a loss map\n", "mass_loss_rates = jnp.linspace(\n", " (2.965e-3 / (1e6 * u.yr) * 15 * u.M_sun).to(code_units.code_mass / code_units.code_time).value,\n", " (2.965e-3 / (1e6 * u.yr) * 70 * u.M_sun).to(code_units.code_mass / code_units.code_time).value,\n", " 60\n", ")\n", "\n", "velocities = jnp.linspace(\n", " (200 * u.km / u.s).to(code_units.code_velocity).value,\n", " (4000 * u.km / u.s).to(code_units.code_velocity).value,\n", " 60\n", ")\n", "\n", "loss_map, vel_list, mass_list = get_loss_map(velocities, mass_loss_rates)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimization" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# We pick gradient descent for pedagogical and visualization reasons.\n", "# In practice one would use e.g. Levenberg-Marquardt from the\n", "# optimistix package.\n", "\n", "def gradient_descent_optimization(func, x_init, learning_rate=20, tol=0.5, max_iter=2000):\n", " xlist = []\n", " x = x_init\n", " loss_list = []\n", " xlist.append(x)\n", "\n", " optimizer = optax.adam(learning_rate=learning_rate)\n", " optimizer_state = optimizer.init(x)\n", "\n", " for _ in range(max_iter):\n", " # Compute the function value and its gradient\n", " loss, f_grad = jax.value_and_grad(func)(x)\n", " loss_list.append(loss)\n", " \n", " # Update the parameter\n", " updates, optimizer_state = optimizer.update(f_grad, optimizer_state)\n", " x = optax.apply_updates(x, updates)\n", " xlist.append(x)\n", " \n", " # Check convergence\n", " if jnp.linalg.norm(updates) < tol:\n", " break\n", " \n", " return x, xlist, loss_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example loss paths" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "initial_guess1 = jnp.array([(1500 * u.km / u.s).to(code_units.code_velocity).value, (2.965e-3 / (1e6 * u.yr) * 30 * u.M_sun).to(code_units.code_mass / code_units.code_time).value])\n", "x1, xlist1, loss_list1 = gradient_descent_optimization(full_profile_loss, initial_guess1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "initial_guess2 = jnp.array([(3500 * u.km / u.s).to(code_units.code_velocity).value, (2.965e-3 / (1e6 * u.yr) * 60 * u.M_sun).to(code_units.code_mass / code_units.code_time).value])\n", "x2, xlist2, loss_list2 = gradient_descent_optimization(full_profile_loss, initial_guess2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "initial_guess3 = jnp.array([(3300 * u.km / u.s).to(code_units.code_velocity).value, (2.965e-3 / (1e6 * u.yr) * 45 * u.M_sun).to(code_units.code_mass / code_units.code_time).value])\n", "x3, xlist3, loss_list3 = gradient_descent_optimization(full_profile_loss, initial_guess3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loss map plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n", "\n", "divider = make_axes_locatable(axs[0])\n", "cax = divider.append_axes('right', size='5%', pad=0.05)\n", "norm = LogNorm(vmin=loss_map.min(), vmax=loss_map.max())\n", "mapper = plt.cm.ScalarMappable(norm=norm, cmap=plt.cm.viridis)\n", "plt.colorbar(mapper, cax=cax, orientation='vertical', label='primitive state loss')\n", "\n", "axs[0].scatter((vel_list * code_units.code_velocity).to(u.km / u.s).value, (mass_list * code_units.code_mass / code_units.code_time).to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value, c=loss_map, cmap='viridis', norm=norm, s = 15, marker = \"s\")\n", "\n", "axs[0].set_xlabel('final wind velocity in km/s')\n", "axs[0].set_ylabel(r'mass of the star in M$_\\odot$')\n", "axs[0].set_title('loss landscape')\n", "\n", "# plot the loss function\n", "axs[1].plot(loss_list1, label='loss 1', color='blue')\n", "\n", "# plot the optimization path\n", "xlist1 = jnp.array(xlist1)\n", "axs[0].plot((xlist1[:, 0] * code_units.code_velocity).to(u.km / u.s).value, (xlist1[:, 1] * code_units.code_mass / code_units.code_time).to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value, color='blue', label='optimization path 1')\n", "axs[0].scatter(\n", " [(xlist1[0, 0] * code_units.code_velocity).to(u.km / u.s).value], [(xlist1[0, 1] * code_units.code_mass / code_units.code_time).to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value],\n", " c='blue', s = 40\n", ")\n", "\n", "# plot the optimization path\n", "xlist2 = jnp.array(xlist2)\n", "axs[0].plot((xlist2[:, 0] * code_units.code_velocity).to(u.km / u.s).value, (xlist2[:, 1] * code_units.code_mass / code_units.code_time).to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value, color='purple', label='optimization path 2')\n", "\n", "axs[0].scatter(\n", " [(xlist2[0, 0] * code_units.code_velocity).to(u.km / u.s).value], [(xlist2[0, 1] * code_units.code_mass / code_units.code_time).to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value],\n", " c='purple', s = 40\n", ")\n", "\n", "# plot the loss function\n", "axs[1].plot(loss_list2, label='loss 2', color='purple')\n", "\n", "# plot the optimization path\n", "xlist3 = jnp.array(xlist3)\n", "axs[0].plot((xlist3[:, 0] * code_units.code_velocity).to(u.km / u.s).value, (xlist3[:, 1] * code_units.code_mass / code_units.code_time).to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value, color='red', label='optimization path 3')\n", "\n", "axs[0].scatter(\n", " [(xlist3[0, 0] * code_units.code_velocity).to(u.km / u.s).value], [(xlist3[0, 1] * code_units.code_mass / code_units.code_time).to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value],\n", " c='red', s = 40\n", ")\n", "\n", "# plot the loss function\n", "axs[1].plot(loss_list3, label='loss 3', color='red')\n", "\n", "axs[1].set_xlabel('iterations')\n", "axs[1].set_ylabel('primitive state loss')\n", "axs[1].set_title('primitive state loss convergence')\n", "axs[1].set_yscale('log')\n", "axs[1].legend(loc='upper right')\n", "plt.tight_layout()\n", "\n", "# mark the true value as a red dot\n", "axs[0].scatter(\n", " [wind_final_velocity.to(u.km / u.s).value],\n", " [wind_mass_loss_rate.to(2.965e-3 / (1e6 * u.yr) * u.M_sun).value],\n", " c='white', s = 200, label='true wind parameters', marker = \"*\", zorder = 10, edgecolors='black'\n", ")\n", "\n", "axs[0].legend()\n", "axs[1].legend()\n", "\n", "# axs 1 y lim 10^6 to 3 * 10^9\n", "if fast_instead_paper:\n", " axs[1].set_ylim(1e5, 3e9)\n", "else:\n", " axs[1].set_ylim(1e6, 3e9)\n", "\n", "plt.savefig('../figures/wind_parameter_optimization.png')" ] } ], "metadata": { "kernelspec": { "display_name": "f1uids", "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": "3.10.15" } }, "nbformat": 4, "nbformat_minor": 2 }