Source code for eulerpi.examples.heat.heat

from typing import Optional

import diffrax
import jax.numpy as jnp
import numpy as np

from eulerpi.core.models import ArtificialModelInterface, JaxModel


[docs] def heat_rhs(t: float, u: jnp.ndarray, args: tuple | list) -> jnp.ndarray: """Right hand side of the heat equation. Args: t (float): time at which the right hand side is evaluated u (jnp.ndarray): current solution of the heat equation args (tuple | list): tuple of the form (dx, dy, param) with dx, dy the spatial discretization and param the thermal conductivity matrix, given as a vector of length 3 with param[0] = kappa_11, param[1] = kappa_22, param[2] = kappa_12 Returns: jnp.ndarray: right hand side of the heat equation """ dx = args[0] dy = args[1] param = args[2] # use the central difference scheme to approximate the derivatives. Gradient preserves the size of the array by using one-sided differences. We throw away the boundary points later. du_dx = jnp.gradient(u, dx, axis=0) du_dx2 = (u[2:, 1:-1] - 2 * u[1:-1, 1:-1] + u[:-2, 1:-1]) / dx**2 du_dy2 = (u[1:-1, 2:] - 2 * u[1:-1, 1:-1] + u[1:-1, :-2]) / dy**2 du_dx_dy = jnp.gradient(du_dx, dy, axis=1) # compute the right hand side of the heat equation rhs = jnp.zeros(u.shape) rhs = rhs.at[1:-1, 1:-1].set( param[0] * du_dx2 + param[1] * du_dy2 + 2 * param[2] * du_dx_dy[1:-1, 1:-1] ) return rhs
[docs] class Heat(JaxModel): """A two-dimensional anisotropic heat conduction equation model on a square domain with four dirichlet boundaries. The model is defined by the following partial differential equation on the square spacial domain :math:`\\Omega = [0, 1]^2` on the time interval :math:`[0, 0.1]`: .. math:: \\frac{\\partial u}{\\partial t} = \\nabla \\cdot \\left( \\kappa \\nabla u \\right) subject to .. math:: u(x, y, t=0) = 0 and .. math:: u(0, y, t) = 1, \\quad u(1, y, t) = 0, \\quad u(x, 0, t) = 1, \\quad u(x, 1, t) = 0 with the symmetric positive definite thermal conductivity matrix :math:`\\kappa` and the temperature :math:`u`. Inference is performed on the entries of :math:`\\kappa`: param[0] = :math:`\\kappa_{11}`, param[1] = :math:`\\kappa_{22}`, param[2] = :math:`\\kappa_{12}`. """ t_end = 0.1 #: End time of the simulation plate_length = jnp.array([1.0, 1.0]) #: Length of the square plate num_grid_points = 20 #: Number of grid points in each spatial direction param_dim = 3 #: Number of independent heat conductivity parameters data_dim = 5 #: The values of the heat equation solution at five points are observed. See evaluation_points. evaluation_points = jnp.array( [ [0.25, 0.25], [0.75, 0.25], [0.5, 0.5], [0.25, 0.75], [0.75, 0.75], ] ) CENTRAL_PARAM = np.array([1.5, 1.5, 0.5]) PARAM_LIMITS = np.array([[1.0, 2.0], [1.0, 2.0], [0.0, 1.0]]) def __init__( self, central_param: np.ndarray = CENTRAL_PARAM, param_limits: np.ndarray = PARAM_LIMITS, name: Optional[str] = None, **kwargs, ) -> None: """Default constructor for the heat model. Args: central_param (np.ndarray, optional): Central parameter within the parameter domain, where the density is non-zero. Defaults to CENTRAL_PARAM. param_limits (np.ndarray, optional): Maximum parameter limits for sampling or grid-based inference. The parameter domain is contained within the box given by param_limits. Defaults to PARAM_LIMITS. name (Optional[str], optional): Name for the model. Defaults to None means the model name is "heat". """ super().__init__(central_param, param_limits, name=name, **kwargs)
[docs] @classmethod def forward(cls, param: np.ndarray) -> np.ndarray: """Forward method for the heat model. Yields the solution of the anisotropic heat conduction equation at time :math:`t=0.1` in five spatial points, which are arranged similar to the number "five" on a dice. Args: param (np.ndarray): Entries of the conductivity matrix: param[0] = :math:`\\kappa_{11}`, param[1] = :math:`\\kappa_{22}`, param[2] = :math:`\\kappa_{12}` Returns: np.ndarray: The solution of the anisotropic heat conduction equation at time :math:`t=0.1` in five spacial points, which are arranged similar to the number "five" on a dice. """ solution = cls.perform_simulation(kappa=param) # linearly interpolate the solution at the evaluation points, use own interpolation function as jax doesn't support scipy.interpolate.interp2d and doesn't provide a 2d interpolation function x = jnp.linspace(0, cls.plate_length[0], cls.num_grid_points) y = jnp.linspace(0, cls.plate_length[1], cls.num_grid_points) # compute the indices between which the evaluation points lie x_indices = jnp.searchsorted(x, cls.evaluation_points[:, 0]) - 1 y_indices = jnp.searchsorted(y, cls.evaluation_points[:, 1]) - 1 dx = cls.plate_length[0] / cls.num_grid_points dy = cls.plate_length[1] / cls.num_grid_points # interpolate the solution at the evaluation points solution_at_evaluation_points = (1 / (dx * dy)) * ( solution[x_indices, y_indices] * (x[x_indices + 1] - cls.evaluation_points[:, 0]) * (y[y_indices + 1] - cls.evaluation_points[:, 1]) + solution[x_indices + 1, y_indices] * (cls.evaluation_points[:, 0] - x[x_indices]) * (y[y_indices + 1] - cls.evaluation_points[:, 1]) + solution[x_indices, y_indices + 1] * (x[x_indices + 1] - cls.evaluation_points[:, 0]) * (cls.evaluation_points[:, 1] - y[y_indices]) + solution[x_indices + 1, y_indices + 1] * (cls.evaluation_points[:, 0] - x[x_indices]) * (cls.evaluation_points[:, 1] - y[y_indices]) ) return solution_at_evaluation_points
[docs] def param_is_within_domain(self, param: np.ndarray) -> bool: """Checks whether a parameter is within the parameter domain of the model. This condition stems from thermodynamical considerations. Args: param(np.ndarray): The parameter to check. Returns: bool: True if the parameter is within the limits. """ return param[0] * param[1] > param[2] ** 2
[docs] @classmethod def perform_simulation(cls, kappa: np.ndarray) -> np.ndarray: """Performs a simulation of the heat equation with the given parameters. Args: kappa (np.ndarray): Entries of the conductivity matrix: kappa[0] = :math:`\\kappa_{11}`, kappa[1] = :math:`\\kappa_{22}`, kapp[2] = :math:`\\kappa_{12}` Returns: np.ndarray: An array containing the solution of the anisotropic heat conduction equation, where the first two indices correspond to the x and y coordinates, respectively. The time is fixed to the class variable t_end. """ # import os # os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"]="false" # The grid x = jnp.linspace(0, cls.plate_length[0], cls.num_grid_points) y = jnp.linspace(0, cls.plate_length[1], cls.num_grid_points) dx = cls.plate_length[0] / cls.num_grid_points dy = cls.plate_length[1] / cls.num_grid_points def stable_time_step(dx, dy, kappa): trace_kappa = kappa[0] + kappa[1] det_kappa = kappa[0] * kappa[1] - kappa[2] ** 2 # Compute the maximum eigenvalue of the thermal conductivity matrix using trace and determinant: kappa_max = 0.5 * ( trace_kappa + jnp.sqrt(trace_kappa**2 - 4 * det_kappa) ) safety_factor = 1.25 dt = jnp.minimum(dx, dy) ** 2 / (safety_factor * 4 * kappa_max) return dt dt0 = stable_time_step(dx, dy, kappa) u0 = jnp.zeros((len(x), len(y))) # Set boundary conditions u0 = u0.at[0, :].set(jnp.ones(len(y))) # left u0 = u0.at[-1, :].set(jnp.zeros(len(y))) # right u0 = u0.at[:, 0].set(jnp.zeros(len(x))) # bottom u0 = u0.at[:, -1].set(jnp.ones(len(x))) # top # perform the time integration using diffrax term = diffrax.ODETerm(heat_rhs) solver = diffrax.Heun() saveat = diffrax.SaveAt(t0=False, t1=True) stepsize_controller = diffrax.PIDController( rtol=1e-5, atol=1e-5, dtmax=dt0 ) sol = diffrax.diffeqsolve( term, solver, t0=0.0, t1=cls.t_end, dt0=dt0, saveat=saveat, y0=u0, stepsize_controller=stepsize_controller, args=(dx, dy, kappa), ) u = sol.ys[-1] # The solution at the final time return u
[docs] class HeatArtificial(Heat, ArtificialModelInterface): CENTRAL_PARAM = np.array([1.5, 1.5, 0.5]) PARAM_LIMITS = np.array([[1.0, 2.0], [1.0, 2.0], [0.0, 1.0]]) num_grid_points = 20 def __init__( self, central_param: np.ndarray = CENTRAL_PARAM, param_limits: np.ndarray = PARAM_LIMITS, name: Optional[str] = None, **kwargs, ) -> None: """Default constructor for the artificial heat model. Args: central_param (np.ndarray, optional): Central parameter within the parameter domain, where the density is non-zero. Defaults to CENTRAL_PARAM. param_limits (np.ndarray, optional): Maximum parameter limits for sampling or grid-based inference. The parameter domain is contained within the box given by param_limits. Defaults to PARAM_LIMITS. name (Optional[str], optional): Name for the model. Defaults to None means the model name is "heat". """ super().__init__(central_param, param_limits, name=name, **kwargs)
[docs] def generate_artificial_params( self, num_samples: int, independent_params: bool = True ) -> np.ndarray: """Generates a set of viable parameter samples for the heat model. Args: num_samples (int): Number of samples to generate. independent_params (bool, optional): Whether the parameters should be independent. Defaults to True. Returns: np.ndarray: A set of viable parameter samples. """ param_spans = self.param_limits[:, 1] - self.param_limits[:, 0] lower_bounds = self.param_limits[:, 0] + 0.2 * param_spans upper_bounds = self.param_limits[:, 1] - 0.2 * param_spans true_param_sample = lower_bounds + ( upper_bounds - lower_bounds ) * np.random.beta(a=2, b=5, size=(num_samples, self.param_dim)) return true_param_sample