deeplearning/modulus/modulus-sym-v120/_modules/modulus/sym/eq/pdes/signed_distance_function.html

Source code for modulus.sym.eq.pdes.signed_distance_function

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"""Screened Poisson Distance 
Equation taken from,
https://www.researchgate.net/publication/266149392_Dynamic_Distance-Based_Shape_Features_for_Gait_Recognition,
Equation 6 in paper.
"""

from sympy import Symbol, Function, sqrt

from modulus.sym.eq.pde import PDE


[docs]class ScreenedPoissonDistance(PDE): """ Screened Poisson Distance Parameters ========== distance : str A user-defined variable for distance. Default is "normal_distance". tau : float A small, positive parameter. Default is 0.1. dim : int Dimension of the Screened Poisson Distance (1, 2, or 3). Default is 3. Example ======== >>> s = ScreenedPoissonDistance(tau=0.1, dim=2) >>> s.pprint() screened_poisson_normal_distance: -normal_distance__x**2 + 0.316227766016838*normal_distance__x__x - normal_distance__y**2 + 0.316227766016838*normal_distance__y__y + 1 """ name = "ScreenedPoissonDistance" def __init__(self, distance="normal_distance", tau=0.1, dim=3): # set params self.distance = distance self.dim = dim # coordinates x, y, z = Symbol("x"), Symbol("y"), Symbol("z") # make input variables input_variables = {"x": x, "y": y, "z": z} if self.dim == 1: input_variables.pop("y") input_variables.pop("z") elif self.dim == 2: input_variables.pop("z") # distance u assert type(distance) == str, "distance needs to be string" distance = Function(distance)(*input_variables) # set equations self.equations = {} sdf_grad = ( 1 - distance.diff(x) ** 2 - distance.diff(y) ** 2 - distance.diff(z) ** 2 ) poisson = sqrt(tau) * ( distance.diff(x, 2) + distance.diff(y, 2) + distance.diff(z, 2) ) self.equations["screened_poisson_" + self.distance] = sdf_grad + poisson
© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.